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Semyonov, S. G., Svitlana Gavrylenko, and Viktor Chelak. "Processing information on the state of a computer system using probabilistic automata." Thesis, Institute of Electrical and Electronics Engineers, 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/40752.
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The paper deals with the processing of information about the state of a computer system using a probabilistic automaton. A model of an intelligent system for detection and classification of malicious software is proposed, which compares a set of features that are characteristic for different classes of viruses with multiple states of the machine. The analysis process is reduced to modeling the operation of the automaton taking into account the probability of transition from state to state, which at each step is recalculated depending on the reaction of the environment. The received results of research allow to reach a conclusion about the possibility of using the offered system for detection of the harmful software.
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Turnes, Junior Pericles do Prado. "Um modelo para avaliar a validade da hipótese de mistura homogênea em sistemas epidemiológicos." Universidade Presbiteriana Mackenzie, 2014. http://tede.mackenzie.br/jspui/handle/tede/1524.
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Previous issue date: 2014-07-29Instituto Presbiteriano Mackenzie
There are many epidemiological models written in terms of ordinary differential equations (ODE). This approach is based on the homogeneous mixing assumption; that is, the topological structure of the network of social contacts, established by the individuals in the population, is not relevant to forecast the propagation of the studied pathogen. In this work, an epidemiological model formulated in terms of ODE and probabilistic cellular automata (PCA) is proposed to study the spread of contagious diseases that do not conferimmunity. The state variables of this model are the percentages of susceptible individuals, infected individuals and empty space. It is shown that this dynamical system can experience Hopf and transcritical bifurcations. Then, this model is used to evaluate the validity of the homogeneous mixing assumption, by using real data related to the transmission of gonorrhea, hepatitis C virus, human immunodeficiency virus and obesity.
Muitos modelos epidemiológicos são escritos em termos de equações diferenciais ordinárias (EDO). Essa abordagem baseia-se no pressuposto de mistura homogênea; ou seja, a estrutura topológica da rede de contatos sociais, estabelecida pelos indivíduos da população, não é relevante para prever o avanço do patógeno em estudo. Neste trabalho, é proposto um modelo epidemiológico formulado em termos de EDO e de autômato celular probabilista (ACP) para estudar a propagação de doenças contagiosas que não conferem imunidade. As variáveis de estado desse modelo são as porcentagens de indivíduos suscetíveis, de indivíduos infectados e de espaço vazio. Mostra-se que esse sistema dinâmico pode apresentar bifurcações de Hopf e transcrítica. O modelo é , então, usado para avaliar a validade da hipótese de mistura homogênea, usando dados relacionados à transmissão de gonorreia, vírus da hepatite C, vírus da imunodeficiência humana e obesidade.
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Casse, Jérôme. "Automates cellulaires probabilistes et processus itérés ad libitum." Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0248/document.
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La première partie de cette thèse porte sur les automates cellulaires probabilistes (ACP) sur la ligne et à deux voisins. Pour un ACP donné, nous cherchons l'ensemble de ces lois invariantes. Pour des raisons expliquées en détail dans la thèse, ceci est à l'heure actuelle inenvisageable de toutes les obtenir et nous nous concentrons, dans cette thèse, surles lois invariantes markoviennes. Nous établissons, tout d'abord, un théorème de nature algébrique qui donne des conditions nécessaires et suffisantes pour qu'un ACP admette une ou plusieurs lois invariantes markoviennes dans le cas où l'alphabet E est fini. Par la suite, nous généralisons ce résultat au cas d'un alphabet E polonais après avoir clarifié les difficultés topologiques rencontrées. Enfin, nous calculons la fonction de corrélation du modèleà 8 sommets pour certaines valeurs des paramètres du modèle en utilisant une partie desrésultats précédentsThe first part of this thesis is about probabilistic cellular automata (PCA) on the line and with two neighbors. For a given PCA, we look for the set of its invariant distributions. Due to reasons explained in detail in this thesis, it is nowadays unthinkable to get all of them and we concentrate our reections on the invariant Markovian distributions. We establish, first, an algebraic theorem that gives a necessary and sufficient condition for a PCA to have one or more invariant Markovian distributions when the alphabet E is finite. Then, we generalize this result to the case of a polish alphabet E once we have clarified the encountered topological difficulties. Finally, we calculate the 8-vertex model's correlation function for some parameters values using previous results.The second part of this thesis is about infinite iterations of stochastic processes. We establish the convergence of the finite dimensional distributions of the α-stable processes iterated n times, when n goes to infinite, according to parameter of stability and to drift r. Then, we describe the limit distributions. In the iterated Brownian motion case, we show that the limit distributions are linked with iterated functions system
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Moraes, Ana Leda Silva. "Avaliando a influência de indivíduos imunes na propagação de doenças contagiosas." Universidade Presbiteriana Mackenzie, 2016. http://tede.mackenzie.br/jspui/handle/tede/1471.
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Previous issue date: 2016-02-01Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Epidemiology is the science that studies the occurrence of diseases in a population. The results of these studies allow a comprehension of a disease propagation and enable actions in order to control epidemics. There are many mathematical models used in epidemiological studies; in which SIR-like models are the most used. In this model, the population is divided into three groups: S - susceptible individuals to infection, I - infected individuals, and R - recovered individuals. The proposal of this thesis is, based on a new SIR model, taking into consideration the effect of recovered individuals on the propagation of contagious diseases and on the recovery of sick individuals. This can be relevant to the study of propagation of typical diseases in children, since immune individuals can catalyze the encounters among susceptible children and infected children, as well as to contribute to the recovery of sick individuals. The predictive ability of the proposed model is evaluated from the records refering to the incidence of chickenpox in Belgium, Germany and Italy, in a pre-vaccination era.
Epidemiologia é a ciência que estuda as ocorrências de doenças numa população. Os resultados desses estudos permitem uma compreensão do comportamento da incidência da doença e possibilita ações a fim de controlar epidemias. Há vários modelos matemáticos que são utilizados para estudos epidemiológicos, sendo modelos do tipo SIR os mais empregados. Nesse modelo, divide-se a população em três classes: 𝑆 - indivíduos suscetíveis à infecção, 𝐼 - indivíduos infectados, e 𝑅 - indivíduos recuperados. A proposta desta dissertação é, a partir de um novo modelo SIR, levar em consideração o efeito de indivíduos recuperados na propagação de doenças contagiosas e na recuperação de indivíduos doentes. Isso pode ser relevante no estudo da propagação de infecções típicas de crianças, já que indivíduos imunes podem servir como catalisador de encontros entre crianças suscetíveis e crianças infectadas, bem como contribuir para a recuperação de indivíduos doentes. A capacidade preditiva do modelo proposto é avaliada a partir dos registros referentes à incidência de varicela na Alemanha, Bélgica e Itália, numa era pré-vacinação.
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Shirmohammadi, Mahsa. "Qualitative analysis of synchronizing probabilistic systems." Thesis, Cachan, Ecole normale supérieure, 2014. http://www.theses.fr/2014DENS0054/document.
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Les Markov Decision Process (MDP) sont des systèmes finis probabilistes avec à la fois des choix aléatoires et des stratégies, et sont ainsi reconnus comme de puissants outils pour modéliser les interactions entre un contrôleur et les réponses aléatoires de l'environment. Mathématiquement, un MDP peut être vu comme un jeu stochastique à un joueur et demi où le contrôleur choisit à chaque tour une action et l'environment répond en choisissant un successeur selon une distribution de probabilités fixée.Il existe deux incomparables représentations du comportement d'un MDP une fois les choix de la stratégie fixés.Dans la représentation classique, un MDP est un générateur de séquences d'états, appelées state-outcome; les conditions gagnantes du joueur sont ainsi exprimées comme des ensembles de séquences désirables d'états qui sont visités pendant le jeu, e.g. les conditions de Borel telles que l'accessibilité. La complexité des problèmes de décision ainsi que la capacité mémoire requise des stratégies gagnantes pour les conditions dites state-outcome ont été déjà fortement étudiées.Depuis peu, les MDPs sont également considérés comme des générateurs de séquences de distributions de probabilités sur les états, appelées distribution-outcome. Nous introduisons des conditions de synchronisation sur les distributions-outcome, qui intuitivement demandent à ce que la masse de probabilité s'accumule dans un (ensemble d') état, potentiellement de façon asymptotique.Une distribution de probabilités est p-synchrone si la masse de probabilité est d'au moins p dans un état; et la séquence de distributions de probabilités est toujours, éventuellement, faiblement, ou fortement p-synchrone si, respectivement toutes, certaines, infiniment plusieurs ou toutes sauf un nombre fini de distributions dans la séquence sont p-synchrones.Pour chaque type de synchronisation, un MDP peut être(i) assurément gagnant si il existe une stratégie qui génère une séquence 1-synchrone;(ii) presque-assurément gagnant si il existe une stratégie qui génère une séquence (1-epsilon)-synchrone et cela pour tout epsilon strictement positif;(iii) asymptotiquement gagnant si pour tout epsilon strictement positif, il existe une stratégie produisant une séquence (1-epsilon)-synchrone.Nous considérons le problème consistant à décider si un MDP est gagnant, pour chaque type de synchronisation et chaque mode gagnant: nous établissons les limites supérieures et inférieures de la complexité de ces problèmes ainsi que la capacité mémoire requise pour une stratégie gagnante optimale.En outre, nous étudions les problèmes de synchronisation pour les automates probabilistes (PAs) qui sont en fait des instances de MDP où les contrôleurs sont restreint à utiliser uniquement des stratégies-mots; c'est à dire qu'ils n'ont pas la possibilité d'observer l'historique de l'exécution du système et ne peuvent connaitre que le nombre de choix effectués jusque là. Les langages synchrones d'un PA sont donc l'ensemble des stratégies-mots synchrones: nous établissons la complexité des problèmes des langages synchrones vides et universels pour chaque mode gagnant.Nous répercutons nos résultats obtenus pour les problèmes de synchronisation sur les MDPs et PAs aux jeux tour à tour à deux joueurs ainsi qu'aux automates finis non-déterministes. En plus de nos résultats principaux, nous établissons de nouveaux résultats de complexité sur les automates finis alternants avec des alphabets à une lettre. Enfin, nous étudions plusieurs variations de synchronisation sur deux instances de systèmes infinis que sont les automates temporisés et pondérésMarkov decision processes (MDPs) are finite-state probabilistic systems with bothstrategic and random choices, hence well-established to model the interactions between a controller and its randomly responding environment.An MDP can be mathematically viewed as a one and half player stochastic game played in rounds when the controller chooses an action,and the environment chooses a successor according to a fixedprobability distribution.There are two incomparable views on the behavior of an MDP, when thestrategic choices are fixed. In the traditional view, an MDP is a generator of sequence of states, called the state-outcome; the winning condition of the player is thus expressed as a set of desired sequences of states that are visited during the game, e.g. Borel condition such as reachability.The computational complexity of related decision problems and memory requirement of winning strategies for the state-outcome conditions are well-studied.Recently, MDPs have been viewed as generators of sequences of probability distributions over states, calledthe distribution-outcome. We introduce synchronizing conditions defined on distribution-outcomes,which intuitively requires that the probability mass accumulates insome (group of) state(s), possibly in limit.A probability distribution is p-synchronizing if the probabilitymass is at least p in some state, anda sequence of probability distributions is always, eventually,weakly, or strongly p-synchronizing if respectively all, some, infinitely many, or all but finitely many distributions in the sequence are p-synchronizing.For each synchronizing mode, an MDP can be (i) sure winning if there is a strategy that produces a 1-synchronizing sequence; (ii) almost-sure winning if there is a strategy that produces a sequence that is, for all epsilon > 0, a (1-epsilon)-synchronizing sequence; (iii) limit-sure winning if for all epsilon > 0, there is a strategy that produces a (1-epsilon)-synchronizing sequence.We consider the problem of deciding whether an MDP is winning, for each synchronizing and winning mode: we establish matching upper and lower complexity bounds of the problems, as well as the memory requirementfor optimal winning strategies.As a further contribution, we study synchronization in probabilistic automata (PAs), that are kind of MDPs where controllers are restricted to use only word-strategies; i.e. no ability to observe the history of the system execution, but the number of choices made so far.The synchronizing languages of a PA is then the set of all synchronizing word-strategies: we establish the computational complexity of theemptiness and universality problems for all synchronizing languages in all winning modes.We carry over results for synchronizing problems from MDPs and PAs to two-player turn-based games and non-deterministic finite state automata. Along with the main results, we establish new complexity results foralternating finite automata over a one-letter alphabet.In addition, we study different variants of synchronization for timed andweighted automata, as two instances of infinite-state systems
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Weidner, Thomas. "Probabilistic Logic, Probabilistic Regular Expressions, and Constraint Temporal Logic." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-208732.
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The classic theorems of Büchi and Kleene state the expressive equivalence of finite automata to monadic second order logic and regular expressions, respectively. These fundamental results enjoy applications in nearly every field of theoretical computer science. Around the same time as Büchi and Kleene, Rabin investigated probabilistic finite automata. This equally well established model has applications ranging from natural language processing to probabilistic model checking.
Here, we give probabilistic extensions Büchi\\\'s theorem and Kleene\\\'s theorem to the probabilistic setting. We obtain a probabilistic MSO logic by adding an expected second order quantifier. In the scope of this quantifier, membership is determined by a Bernoulli process. This approach turns out to be universal and is applicable for finite and infinite words as well as for finite trees. In order to prove the expressive equivalence of this probabilistic MSO logic to probabilistic automata, we show a Nivat-theorem, which decomposes a recognisable function into a regular language, homomorphisms, and a probability measure.
For regular expressions, we build upon existing work to obtain probabilistic regular expressions on finite and infinite words. We show the expressive equivalence between these expressions and probabilistic Muller-automata. To handle Muller-acceptance conditions, we give a new construction from probabilistic regular expressions to Muller-automata. Concerning finite trees, we define probabilistic regular tree expressions using a new iteration operator, called infinity-iteration. Again, we show that these expressions are expressively equivalent to probabilistic tree automata.
On a second track of our research we investigate Constraint LTL over multidimensional data words with data values from the infinite tree. Such LTL formulas are evaluated over infinite words, where every position possesses several data values from the infinite tree. Within Constraint LTL on can compare these values from different positions. We show that the model checking problem for this logic is PSPACE-complete via investigating the emptiness problem of Constraint Büchi automata.
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Louis, Pierre-Yves. "Increasing coupling for probabilistic cellular automata." Universität Potsdam, 2005. http://opus.kobv.de/ubp/volltexte/2006/659/.
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We give a necessary and sufficient condition for the existence of an increasing coupling of N (N >= 2) synchronous dynamics on S-Zd (PCA). Increasing means the coupling preserves stochastic ordering. We first present our main construction theorem in the case where S is totally ordered; applications to attractive PCAs are given. When S is only partially ordered, we show on two examples that a coupling of more than two synchronous dynamics may not exist. We also prove an extension of our main result for a particular class of partially ordered spaces.
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Khapko, Taras. "Edge states and transition to turbulence in boundary layers." Doctoral thesis, KTH, Stabilitet, Transition, Kontroll, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186038.
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The focus of this thesis is the numerical study of subcritical transition to turbulence in boundary-layer flows. For the most part, boundary layers with uniform suction are considered. Constant homogeneous suction counteracts the spatial growth of the boundary layer, rendering the flow parallel. This enables research approaches which are not feasible in the context of spatially developing flows. In the first part, the laminar–turbulent separatrix of the asymptotic suction boundary layer (ASBL) is investigated numerically by means of an edge-tracking algorithm. The obtained edge states experience recurrent dynamics, going through calm and bursting phases. The self-sustaining mechanism bears many similarities with the classical regeneration cycle of near-wall turbulence. The recurrent simple structure active during calm phases is compared to the nucleation of turbulence events in bypass transition originating from delocalised initial conditions. The implications on the understanding of the bypass-transition process and the edge state's role are discussed. Based on this understanding, a model is constructed which predicts the position of the nucleation of turbulent spots during free-stream turbulence induced transition in spatially developing boundary-layer flow. This model is used together with a probabilistic cellular automaton (PCA), which captures the spatial spreading of the spots, correctly reproducing the main statistical characteristics of the transition process. The last part of the thesis is concerned with the spatio-temporal aspects of turbulent ASBL in extended numerical domains near the onset of sustained turbulence. The different behaviour observed in ASBL, i.e. absence of sustained laminar–turbulent patterns, which have been reported in other wall-bounded flows, is associated with different character of the large-scale flow. In addition, an accurate quantitative estimate for the lowest Reynolds number with sustained turbulence is obtainedQC 20160429
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Kelmendi, Edon. "Two-Player Stochastic Games with Perfect and Zero Information." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0238/document.
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On considère des jeux stochastiques joués sur un graphe fini. La première partie s’intéresse aux jeux stochastiques à deux joueurs et information parfaite. Dans de tels jeux, les joueurs choisissent des actions dans ensemble fini, tour à tour, pour une durée infinie, produisant une histoire infinie. Le but du jeu est donné par une fonction d’utilité qui associe un réel à chaque histoire, la fonction est bornée et Borel-mesurable. Le premier joueur veut maximiser l’utilité espérée, et le deuxième joueur veut la minimiser. On démontre que si la fonction d’utilité est à la fois shift-invariant et submixing alors le jeu est semi-positionnel. C’est-à-dire le premier joueur a une stratégie optimale qui est déterministe et sans mémoire. Les deux joueurs ont information parfaite: ils choisissent leurs actions en ayant une connaissance parfaite de toute l’histoire. Dans la deuxième partie, on étudie des jeux de durée fini où le joueur protagoniste a zéro information. C’est-à-dire qu’il ne reçoit aucune information sur le déroulement du jeu, par conséquent sa stratégie est un mot fini sur l’ensemble des actions. Un automates probabiliste peut être considéré comme un tel jeu qui a un seul joueur. Tout d’abord, on compare deux classes d’automates probabilistes pour lesquelles le problème de valeur 1 est décidable: les automates leaktight et les automates simples. On prouve que la classe des automates simples est un sous-ensemble strict de la classe des automates leaktight. Puis, on considère des jeux semi-aveugles, qui sont des jeux à deux joueurs où le maximiseur a zéro information, et le minimiseur est parfaitement informé. On définit la classe des jeux semi-aveugles leaktight et on montre que le problème d’accessibilité maxmin est décidable sur cette classeWe consider stochastic games that are played on finite graphs. The subject of the first part are two-player stochastic games with perfect information. In such games the two players take turns choosing actions from a finite set, for an infinite duration, resulting in an infinite play. The objective of the game is given by a Borel-measurable and bounded payoff function that maps infinite plays to real numbers. The first player wants to maximize the expected payoff, and the second player has the opposite objective, that of minimizing the expected payoff. We prove that if the payoff function is both shift-invariant and submixing then the game is half-positional. This means that the first player has an optimal strategy that is at the same time pure and memoryless. Both players have perfect information, so the actions are chosen based on the whole history. In the second part we study finite-duration games where the protagonist player has zero information. That is, he gets no feedback from the game and consequently his strategy is a finite word over the set of actions. Probabilistic finite automata can be seen as an example of such a game that has only a single player. First we compare two classes of probabilistic automata: leaktight automata and simple automata, for which the value 1 problem is known to be decidable. We prove that simple automata are a strict subset of leaktight automata. Then we consider half-blind games, which are two player games where the maximizer has zero information and the minimizer is perfectly informed. We define the class of leaktight half-blind games and prove that it has a decidable maxmin reachability problem
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Coore, Daniel. "Automatic profiler-driven probabilistic compiler optimization." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/35396.
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Louis, Pierre-Yves. "Increasing Coupling of Probabilistic Cellular Automata." Universität Potsdam, 2004. http://opus.kobv.de/ubp/volltexte/2011/5157/.
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We give a necessary and sufficient condition for the existence of an increasing coupling of N (N >= 2) synchronous dynamics on S-Zd (PCA). Increasing means the coupling preserves stochastic ordering. We first present our main construction theorem in the case where S is totally ordered; applications to attractive PCAs are given. When S is only partially ordered, we show on two examples that a coupling of more than two synchronous dynamics may not exist. We also prove an extension of our main result for a particular class of partially ordered spaces.
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Hughes, Nicholas Peter. "Probabilistic models for automated ECG interval analysis." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433475.
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Paige, Timothy Brooks. "Automatic inference for higher-order probabilistic programs." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:d912c4de-4b08-4729-aa19-766413735e2a.
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Probabilistic models used in quantitative sciences have historically co-evolved with methods for performing inference: specific modeling assumptions are made not because they are appropriate to the application domain, but because they are required to leverage existing software packages or inference methods. The intertwined nature of modeling and computational concerns leaves much of the promise of probabilistic modeling out of reach for data scientists, forcing practitioners to turn to off-the-shelf solutions. The emerging field of probabilistic programming aims to reduce the technical and cognitive overhead for writing and designing novel probabilistic models, by introducing a specialized programming language as an abstraction barrier between modeling and inference. The aim of this thesis is to develop inference algorithms that scale well and are applicable to broad model families. We focus particularly on methods that can be applied to models written in general-purpose higher-order probabilistic programming languages, where programs may make use of recursion, arbitrary deterministic simulation, and higher-order functions to create more accurate models of an application domain. In a probabilistic programming system, probabilistic models are defined using a modeling language; a backend implements generic inference methods applicable to any model written in this language. Probabilistic programs - models - can be written without concern for how inference will later be performed. We begin by considering several existing probabilistic programming languages, their design choices, and tradeoffs. We then demonstrate how programs written in higher-order languages can be used to define coherent probability models, describing possible approaches to inference, and providing explicit algorithms for efficient implementations of both classic and novel inference methods based on and extending sequential Monte Carlo. This is followed by an investigation into the use of variational inference methods within higher-order probabilistic programming languages, with application to policy learning, adaptive importance sampling, and amortization of inference.
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Hošták, Viliam Samuel. "Učení se automatů pro rychlou detekci anomálií v síťovém provozu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-449296.
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The focus of this thesis is the fast network anomaly detection based on automata learning. It describes and compares several chosen automata learning algorithms including their adaptation for the learning of network characteristics. In this work, various network anomaly detection methods based on learned automata are proposed which can detect sequential as well as statistical anomalies in target communication. For this purpose, they utilize automata's mechanisms, their transformations, and statistical analysis. Proposed detection methods were implemented and evaluated using network traffic of the protocol IEC 60870-5-104 which is commonly used in industrial control systems.
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Srivastava, Saket. "Probabilistic modeling of quantum-dot cellular automata." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002399.
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Henry, Melvin Michael 1968. "Model-based estimation of probabilistic hybrid automata." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/82249.
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Kilian, Joe. "Two undecidability results in probabilistic automata theory." Thesis, Massachusetts Institute of Technology, 1985. https://hdl.handle.net/1721.1/126347.
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Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1985.Bibliography: leaf 13.
by Joseph J. Kilian.
Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1985.
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Sznajder-Glodowski, Malgorzata. "Categorical approach to automata theory." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=65417.
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Shah, Shishir Kirit. "Probabilistic multifeature/multisensor integration for automatic object recognition /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
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Rainforth, Thomas William Gamlen. "Automating inference, learning, and design using probabilistic programming." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:e276f3b4-ff1d-44bf-9d67-013f68ce81f0.
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Imagine a world where computational simulations can be inverted as easily as running them forwards, where data can be used to refine models automatically, and where the only expertise one needs to carry out powerful statistical analysis is a basic proficiency in scientific coding. Creating such a world is the ambitious long-term aim of probabilistic programming. The bottleneck for improving the probabilistic models, or simulators, used throughout the quantitative sciences, is often not an ability to devise better models conceptually, but a lack of expertise, time, or resources to realize such innovations. Probabilistic programming systems (PPSs) help alleviate this bottleneck by providing an expressive and accessible modeling framework, then automating the required computation to draw inferences from the model, for example finding the model parameters likely to give rise to a certain output. By decoupling model specification and inference, PPSs streamline the process of developing and drawing inferences from new models, while opening up powerful statistical methods to non-experts. Many systems further provide the flexibility to write new and exciting models which would be hard, or even impossible, to convey using conventional statistical frameworks. The central goal of this thesis is to improve and extend PPSs. In particular, we will make advancements to the underlying inference engines and increase the range of problems which can be tackled. For example, we will extend PPSs to a mixed inference-optimization framework, thereby providing automation of tasks such as model learning and engineering design. Meanwhile, we make inroads into constructing systems for automating adaptive sequential design problems, providing potential applications across the sciences. Furthermore, the contributions of the work reach far beyond probabilistic programming, as achieving our goal will require us to make advancements in a number of related fields such as particle Markov chain Monte Carlo methods, Bayesian optimization, and Monte Carlo fundamentals.
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Al, Marhubi Kamal Amran. "Duality and finality for deterministic and probabilistic automata." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86515.
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Duality theory is an important topic within automata theory which leads to a tight relationship between automata and modal logics. These dualities are very much in the spirit of the classical Stone type dualities. In a recent paper by Hundt et al. this was explored in the context of probabilistic automata equipped with a notion of observations. The motivation there was to understand how to reason about systems with hidden state. It turned out to be problematic to formalise this properly in categorical terms and the intuitive notion of duality presented there did not seem to correspond well with what was expected.We study this putative duality coalgebraically. Towards this, we review coalgebras and view automata as coalgebras for a functor. This provides a natural way to consider categories of automata. We give an alternative construction of the final Kripke automaton of Cordy, which allows us to make the connection to duality. We show that the duality construction of Hundt et al. is in fact a finality construction, and demonstrate an analogous correspondence in the probabilistic case.
La théorie de la dualité est un sujet important dans la théorie des automates qui mène à une étroite relation entre les automates et les logiques modales. Ces dualités sont tout à fait dans l'esprit des dualités classiques de type Stone. Dans un document récent de Hundt et al. cela a été étudié dans le cadre des automates probabilistes équipés d'une notion d'observation. Leur motivation a été de comprendre comment raisonner sur les systèmes avec des états cachés. Il s'est avéré difficile de formaliser cela correctement en utilisant la théorie des catégories, et la notion intuitive de la dualité qui a été présenté ne semblent pas bien correspondre à ce qui était attendu.
Nous étudions cette dualité coalgébriquement. à cette fin, nous passons en revue les coalgèbres et nous regardons les automates comme coalgèbres pour un foncteur. Cela fournit un moyen naturel de considérer les catégories d'automates. Nous donnons une construction alternative de l'automate Kripke final de Cordy, qui nous permet de faire la connexion à la dualité. Nous montrons que la construction de la dualité de Hundt et al. est en fait une construction de finalité, et démontrons une correspondance analogue dans la cas probabiliste.
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Pilling, Mark Andrew. "Probabilistic cellular automata and competition across tropic levels." Thesis, University of Sheffield, 2001. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:124385.
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This thesis investigates a resource driven probabilistic cellular automata (PCA) model of plant competition in terms of local interactions, spatial distributions, and invasion. The model also incorporates herbivores and carnivores and examines their effect on plant populations and community structure. Comparisons are drawn between the model, field studies and other mathematical models. Chapter 1 provides a background of relevant concepts from plants and animal ecology, details a number of mathematical models used in this field and describes the model relevant models and results in the literature. It concludes with a comparison of the features of the most germane models and field studies. Chapter 2 primarily focuses on plants, argues for the model we have chosen, recaptures previous results which are similar to some natural phenomena, and makes a preliminary investigation of community behaviour and disturbance. It then describes the effect of introducing biomass for plants on species behaviour, and their spatial distributions. Chapter 3 deals with competition between different species, and aspects of invasion. Coexistence between functionally different plants can occur, join count statistics and measures for patch location on the torus are developed and applied. Chapter 4 derives a generalised probabilistic model for ruderal monocultures, finds numerical solutions for these and investigates models for vegetatively growing species of plants. Chapter 5 examines the population effects of herbivory (i.e. importance of spatial correlation of disturbance) and analogies to competitor-stress tolerator-ruderal (CSR) primary plant types, as well as plant successional rates and factors affecting community composition. Equilibrium species composition corresponded to CSR theory when plant immigration was introduced. Chapter 6 investigates the basic effects of carnivory, and discusses parallels between probabilistic cellular automata and field studies.
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Martin, Oliver B. 1979. "Accurate belief state update for probabilistic constraint automata." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32446.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.Includes bibliographical references (p. 91-93).
As autonomous spacecraft and other robotic systems grow increasingly complex, there is a pressing need for capabilities that more accurately monitor and diagnose system state while maintaining reactivity. Mode estimation addresses this problem by reasoning over declarative models of the physical plant, represented as a factored variant of Hidden Markov Models (HMMs), called Probabilistic Concurrent Constraint Automata (PCCA). Previous mode estimation approaches track a set of most likely PCCA state trajectories, enumerating them in order of trajectory probability. Although Best-First Trajectory Enumeration (BFTE) is efficient, ignoring the additional trajectories that lead to the same target state can significantly underestimate the true state probability and result in misdiagnosis. This thesis introduces two innovative belief state approximation techniques, called Best-First Belief State Enumeration (BFBSE) and Best-First Belief State Update (BFBSU), that address this limitation by computing estimate probabilities directly from the HMM belief state update equations. Theoretical and empirical results show that I3FBSE and BFBSU significantly increases estimator accuracy, uses less memory, and have no increase in computation time when enumerating a moderate number of estimates for the approximate belief state of subsystem sized models.
by Oliver Borelli Martin.
S.M.
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FRANCH, Daniel Kudlowiez. "Dynamical system modeling with probabilistic finite state automata." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/25448.
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FACEPE
Discrete dynamical systems are widely used in a variety of scientific and engineering applications, such as electrical circuits, machine learning, meteorology and neurobiology. Modeling these systems involves performing statistical analysis of the system output to estimate the parameters of a model so it can behave similarly to the original system. These models can be used for simulation, performance analysis, fault detection, among other applications. The current work presents two new algorithms to model discrete dynamical systems from two categories (synchronizable and non-synchronizable) using Probabilistic Finite State Automata (PFSA) by analyzing discrete symbolic sequences generated by the original system and applying statistical methods and inference, machine learning algorithms and graph minimization techniques to obtain compact, precise and efficient PFSA models. Their performance and time complexity are compared with other algorithms present in literature that aim to achieve the same goal by applying the algorithms to a series of common examples.
Sistemas dinâmicos discretos são amplamente usados em uma variedade de aplicações cientifícas e de engenharia, por exemplo, circuitos elétricos, aprendizado de máquina, meteorologia e neurobiologia. O modelamento destes sistemas envolve realizar uma análise estatística de sequências de saída do sistema para estimar parâmetros de um modelo para que este se comporte de maneira similar ao sistema original. Esses modelos podem ser usados para simulação, referência ou detecção de falhas. Este trabalho apresenta dois novos algoritmos para modelar sistemas dinâmicos discretos de duas categorias (sincronizáveis e não-sincronizáveis) por meio de Autômatos Finitos Probabilísticos (PFSA, Probabilistic Finite State Automata) analisando sequências geradas pelo sistema original e aplicando métodos estatísticos, algoritmos de aprendizado de máquina e técnicas de minimização de grafos para obter modelos PFSA compactos e eficientes. Sua performance e complexidade temporal são comparadas com algoritmos presentes na literatura que buscam atingir o mesmo objetivo aplicando os algoritmos a uma série de exemplos.
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Klinov, Pavel. "Practical reasoning in probabilistic description logic." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/practical-reasoning-in-probabilistic-description-logic(6aff2ad0-dc76-44cf-909b-2134f580f29b).html.
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Description Logics (DLs) form a family of languages which correspond to decidable fragments of First-Order Logic (FOL). They have been overwhelmingly successful for constructing ontologies - conceptual structures describing domain knowledge. Ontologies proved to be valuable in a range of areas, most notably, bioinformatics, chemistry, Health Care and Life Sciences, and the Semantic Web.One limitation of DLs, as fragments of FOL, is their restricted ability to cope with various forms of uncertainty. For example, medical knowledge often includes statistical relationships, e.g., findings or results of clinical trials. Currently it is maintained separately, e.g., in Bayesian networks or statistical models. This often hinders knowledge integration and reuse, leads to duplication and, consequently, inconsistencies.One answer to this issue is probabilistic logics which allow for smooth integration of classical, i.e., expressible in standard FOL or its sub-languages, and uncertain knowledge. However, probabilistic logics have long been considered impractical because of discouraging computational properties. Those are mostly due to the lack of simplifying assumptions, e.g., independence assumptions which are central to Bayesian networks.In this thesis we demonstrate that deductive reasoning in a particular probabilistic DL, called P-SROIQ, can be computationally practical. We present a range of novel algorithms, in particular, the probabilistic satisfiability procedure (PSAT) which is, to our knowledge, the first scalable PSAT algorithm for a non-propositional probabilistic logic. We perform an extensive performance and scalability evaluation on different synthetic and natural data sets to justify practicality.In addition, we study theoretical properties of P-SROIQ by formally translating it into a fragment of first-order logic of probability. That allows us to gain a better insight into certain important limitations of P-SROIQ. Finally, we investigate its applicability from the practical perspective, for instance, use it to extract all inconsistencies from a real rule-based medical expert system.We believe the thesis will be of interest to developers of probabilistic reasoners. Some of the algorithms, e.g., PSAT, could also be valuable to the Operations Research community since they are heavily based on mathematical programming. Finally, the theoretical analysis could be helpful for designers of future probabilistic logics.
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Chapman, Gary. "Computer-based musical composition using a probabilistic algorithmic method." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341603.
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Sproston, Jeremy James. "Model checking of probabilistic timed and hybrid systems." Thesis, University of Birmingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391021.
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Timmons, Eric (Eric M. ). "Fast, approximate state estimation of concurrent probabilistic hybrid automata." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82494.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from department-submitted PDF version of thesis
Includes bibliographical references (p. 73).
It is an undeniable fact that autonomous systems are simultaneously becoming more common place, more complex, and deployed in more inhospitable environments. Examples include smart homes, smart cars, Mars rovers, unmanned aerial vehicles, and autonomous underwater vehicles. A common theme that all of these autonomous systems share is that in order to appropriately control them and prevent mission failure, they must be able to quickly estimate their internal state and the state of the world. A natural representation of many real world systems is to describe them in terms of a mixture of continuous and discrete variables. Unfortunately, hybrid estimation is typically intractable due to the large space of possible assignments to the discrete variables. In this thesis, we investigate how to incorporate conflict directed techniques from the consistency-based, model-based diagnosis community into a hybrid framework that is no longer purely consistency based. We introduce a novel search algorithm, A* with Bounding Conflicts, that uses conflicts to not only record infeasiblilities, but also learn where in the search space the heuristic function provided to the A* search is weak (possibly due to heavy to moderate sensor or process noise). Additionally, we describe a hybrid state estimation algorithm that uses this new search to perform estimation on hybrid discrete/continuous systems.
by Eric Timmons.
S.M.
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Regnault, Damien. "Sur les automates cellulaires probabilistes : comportements asynchrones." Lyon, Ecole normale supérieure, 2008. http://www.theses.fr/2008ENSLA480.
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König, Johan. "Analyzing Substation Automation System Reliability using Probabilistic Relational Models and Enterprise Architecture." Doctoral thesis, KTH, Industriella informations- och styrsystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-145006.
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Modern society is unquestionably heavily reliant on supply of electricity. Hence, the power system is one of the important infrastructures for future growth. However, the power system of today was designed for a stable radial flow of electricity from large power plants to the customers and not for the type of changes it is presently being exposed to, like large scale integration of electric vehicles, wind power plants, residential photovoltaic systems etc. One aspect of power system control particular exposed to these changes is the design of power system control and protection functionality. Problems occur when the flow of electricity changes from a unidirectional radial flow to a bidirectional. Such an implication requires redesign of control and protection functionality as well as introduction of new information and communication technology (ICT). To make matters worse, the closer the interaction between the power system and the ICT systems the more complex the matter becomes from a reliability perspective. This problem is inherently cyber-physical, including everything from system software to power cables and transformers, rather than the traditional reliability concern of only focusing on power system components. The contribution of this thesis is a framework for reliability analysis, utilizing system modeling concepts that supports the industrial engineering issues that follow with the imple-mentation of modern substation automation systems. The framework is based on a Bayesian probabilistic analysis engine represented by Probabilistic Relational Models (PRMs) in com-bination with an Enterprise Architecture (EA) modeling formalism. The gradual development of the framework is demonstrated through a number of application scenarios based on substation automation system configurations. This thesis is a composite thesis consisting of seven papers. Paper 1 presents the framework combining EA, PRMs and Fault Tree Analysis (FTA). Paper 2 adds primary substation equipment as part of the framework. Paper 3 presents a mapping between modeling entities from the EA framework ArchiMate and substation automation system configuration objects from the IEC 61850 standard. Paper 4 introduces object definitions and relations in coherence with EA modeling formalism suitable for the purpose of the analysis framework. Paper 5 describes an extension of the analysis framework by adding logical operators to the probabilistic analysis engine. Paper 6 presents enhanced failure rates for software components by studying failure logs and an application of the framework to a utility substation automation system. Finally, Paper 7 describes the ability to utilize domain standards for coherent modeling of functions and their interrelations and an application of the framework utilizing software-tool support.QC 20140505
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Troina, Angelo [Verfasser]. "Probabilistic Timed Automata for Security Analysis and Design / Angelo Troina." Munich : GRIN Publishing, 2017. http://d-nb.info/1138030554/34.
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Naumann, Maximilian [Verfasser], and C. [Akademischer Betreuer] Stiller. "Probabilistic Motion Planning for Automated Vehicles / Maximilian Naumann ; Betreuer: C. Stiller." Karlsruhe : KIT-Bibliothek, 2020. http://d-nb.info/121859974X/34.
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Esposito, Yann. "Contribution à l'inférence d'automates probabilistes." Aix-Marseille 1, 2004. http://www.theses.fr/2004AIX11040.
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Les langages stochastiques -- des disbributions de probabilité sur des séquences -- jouent un rôle central dans de nombreux domaines. Notamment en bio-informatique, en reconnaissance vocale et plus généralement dans la plupart des domaines qui traitent un grand nombre de données représentées comme des chaînes de caractères. Nous utilisons des automates probabilistes (PA) -- un modèle équivalent aux chaînes de Markov cachées -- pour modéliser de tels langages stochastiques. Inférer des automates probabilistes à partir de données est un vaste domaine de recherche ouvert. Nous montrons qu'à partir d'une séquence de mots indépendamment et identiquement distribués selon un langage stochastique modélisé par un PA, on peut identifier celui-ci à la limite avec probabilité 1. Pour cela nous utilisons un algorithme d'inférence de PA qui n'a pas un comportement énumératif. Malheureusement, la complexité de cet algorithme est trop importante pour pouvoir en envisager une utilisation réelle. Il semble alors naturel d'essayer de trouver une sous-classe, la plus grande possible, pour laquelle nous pouvons concevoir un algorithme utilisable. Aussi, introduisons-nous une nouvelle classe d'automates probabilistes : les automates probabilistes à résiduels (PRA). La définition des PRA est basée sur une notion intrinsèque des langages stochastiques : les résiduels. Nous montrons que les PRA sont plus concis et plus expressifs que les automates probabilistes déterministes (PDA) -- une sous-classe des PA pour laquelle des algorithmes d'inférence existe. Comme les PDA, les PRA possèdent une forme canonique. De l'algorithme d'inférence de PA, nous en déduisons un algorithme d'inférence de PRA utilisable. On peut aussi modéliser les langages stochastiques avec des automates à multiplicité. Il s'agit d'un modèle qui généralise la notion de PA. Nous montrons que la classe des langages stochastiques modélisables par des MA est strictement plus grande que celle modélisée par des PA. Nous montrons de plus que l'on ne peut pas décider si un MA modélise un langage stochastique. Ce résultat négatif associé au manque de robustesse de la représentation des langages stochastiques par des MA nous permet de conclure qu'il n'existe probablement pas d'algorithme d'inférence de MA utilisable. Les MA ne semblent donc pas être une bonne représentation pour l'inférence de langages stochastiques.
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CALOMENI, ANTONIO LUIZ VITALO. "AUTOMATIC AND ASSISTED NAVIGATION ON BLACK OIL RESERVOIRS BASED ON PROBABILISTIC ROADMAPS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=6683@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOHá muitas aplicações de realidade virtual que requerem navegação em ambientes 3D. Em geral, esta facilidade é oferecida permitindo que o usuário controle todos os seis graus de liberdade da câmera virtual. No entanto, dessa forma é difícil, mesmo para usuários experientes, chegar a áreas de interesse com movimentos suaves e livres de colisão. Torna-se necessário prover uma navegação assistida, onde o usuário guia a câmera mais facilmente, sem restringir sua exploração do ambiente. Além disso, é interessante prover uma navegação totalmente automática, onde o usuário seleciona um alvo e o sistema calcula um caminho suave e livre de colisões através do ambiente. Este trabalho propõe uma solução específica para navegação em modelos usados para simulação numérica de reservatórios naturais de petróleo, baseada em grafos de guia probabilísticos. Utilizados comumente em aplicações de robótica, grafos de guia probabilísticos visam capturar, através de uma amostragem aleatória, a conectividade das áreas livres da cena, sendo construídos em pré-processamento e consultados em tempo de execução. Este trabalho propõe um algoritmo de construção de grafo de guia específico para reservatórios e duas técnicas para utilizar o grafo obtido como base para prover navegação automática e navegação assistida.
There are many virtual reality applications that require navigation on 3D environments. Usually, this feature is offered by allowing the user to control all the six degrees of freedom of the virtual camera. However, it is difficult in this way, even for experienced users, to reach areas of interest with smooth and collision-free paths. It becomes necessary to provide an assisted navigation, in which the user guides the camera more easily, without restricting the environment exploration. Moreover, it is interesting to provide a fully automatic navigation, in which the user selects a target and the system computes a smooth, collision-free path throughout the environment. This work proposes a specific solution for navigation on models used in numerical simulations of black oil reservoirs, based on probabilistic roadmaps. Commonly used in robotics applications, probabilistic roadmaps tend to capture, by random sampling, the conectivity of the free space of the scene, being constructed in a pre-processing stage and queried in execution time. This work proposes a roadmap construction algorithm specific to reservoirs and two techniques to use the obtained roadmap as the basis to provide assisted and automatic navigation.
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Schwarz, Robert E. (Robert Erik). "A probabilistic model of the effects of satellite system automation on cost and availability." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10511.
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Marques, Henrique Costa. "An inference model with probabilistic ontologies to support automation in effects-based operations planning." Instituto Tecnológico de Aeronáutica, 2012. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=2190.
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In modern day operations, planning has been an increasingly complex activity. This is especially true in scenarios where there is an interaction between civilian and military organizations, involving multiple actors in a diversified way, with the intertwining requirements that limit the solution space in non-trivial ways. Under these circumstances, decision support systems are an essential tool that can also become a problem if not properly used. Although this has been widely recognized by the planning and decision support systems communities, there has been little progress in designing a comprehensive methodology for course of action (COA) representation that supports the diverse aspects of the Command and Control planning cycle in Effects-Based Operations (EBO). This work proposes an approach based on probabilistic ontologies capable to support task planning cycle in EBO at the Command and Control tactical planning level. At this level, we need to specify the tasks that will possibly achieve the desired effects defined by the upper echelon, with uncertainty not only in the execution, but also from the environment parameters. Current approaches suggest solutions to the operational level, giving greater importance to the process of targeting while approaches to the tactical level do not take into account the uncertainty present in the environment and actions in their ability to achieve the desired effect. To offer a possible solution to knowledge representation at the tactical level, an inference model was developed to generate the planning problem to be sent to a planning system. The proposed model also describes simulation as a tool to assist the plan';s refinement. The main contribution of this work is the development of a process of probabilistic inference against a knowledge base that is capable of dealing with uncertainty at the tactical level, where different tasks can achieve the same effect, but with different probabilities of success. Obtained results indicate the feasibility of the proposal once valid plans are generated in reasonable time from general orders or requests.
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Kalgaonkar, Kaustubh. "Probabilistic space maps for speech with applications." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42739.
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The objective of the proposed research is to develop a probabilistic model of speech production that exploits the multiplicity of mapping between the vocal tract area functions (VTAF) and speech spectra. Two thrusts are developed. In the first, a latent variable model that captures uncertainty in estimating the VTAF from speech data is investigated. The latent variable model uses this uncertainty to generate many-to-one mapping between observations of the VTAF and speech spectra. The second uses the probabilistic model of speech production to improve the performance of traditional speech algorithms, such as enhancement, acoustic model adaptation, etc.
In this thesis, we propose to model the process of speech production with a probability map. This proposed model treats speech production as a probabilistic process with many-to-one mapping between VTAF and speech spectra. The thesis not only outlines a statistical framework to generate and train these probabilistic models from speech, but also demonstrates its power and flexibility with such applications as enhancing speech from both perceptual and recognition perspectives.
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Yu, Han Baek. "Combinatorial and probabilistic aspects of coupled oscillators." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524195989591036.
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Chamseddine, Najla. "Analyse quantitative paramétrée d'automates temporisés probabilistes." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2009. http://tel.archives-ouvertes.fr/tel-00626062.
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Nous considérons une sous classe d'automates temporisés probabilistes où les contraintes temporelles au niveau des gardes et des invariants sont exprimées par des paramètres. Cette sous classe est appelée la classe des automates Temporisés Probabilistes Paramétrés Semi Déterminés (ATPP Semi Déterminés). Cette classe d'automates se définit en particulier par l'attribution d'une unique distribution à chaque état et par des gardes de la forme x<=a où a est un paramètre ou un entier naturel. Nous imposons de plus deux propriétés sur ces automates qui sont celles de non blocage et fortement non zenon. Notre travail vise à calculer le temps moyen maximal de convergence vers un état dit absorbant q_end dans ce type d'automates. L'unique méthode traitant déjà ce type de problème fait appel à la discrétisation du temps et à l'application de techniques de programmation linéaire. Elle est cependant exponentielle car elle dépend du nombre d'horloges et de la plus grande constante à laquelle sont comparées les horloges, lors de la discrétisation. Le graphe résultant peut être de taille exponentielle. Pour tout ATPP Semi Déterminé, on définit un automate totalement déterministe, appelé ATPP Déterminé, en remplaçant toute garde de la forme x<=a par une garde de la forme x=a. Le temps d'attente en chaque état est ainsi fixé par la valuation de l'état initial qui remet toutes les horloges à zéro. Nous démontrons que le temps moyen de convergence vers q_end dans l'ATPP Déterminé est égal au temps moyen maximal de convergence dans l'ATPP Semi Déterminé dont il découle. Pour calculer le temps moyen de convergence vers q_end nous construisons à partir de l'ATPP Déterminé un graphe appelé "graphe des macro-steps" qui contient de façon concise l'information nécessaire au calcul du coût moyen de convergence vers q_end. Ce graphe est de taille polynomiale et se construit en temps polynomial. Le calcul du temps moyen de convergence dans le graphe des macro-steps est solution d'un système linéaire, comme dans le cas des chaînes de Markov avec coûts. On résout ce système linéaire en temps polynomial, ce qui permet d'obtenir finalement le temps moyen maximal de convergence vers q_end dans l'ATPP Semi Déterminé. Nous appliquons enfin cette méthode à certains protocoles de communication, notamment BRP (Bounded Retransmission Protocol) et CSMA/CD (Carrier Sense Multiple Access with Collision Detection).
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Naumann, Maximilian [Verfasser], Christoph [Akademischer Betreuer] Stiller, and Mykel J. [Akademischer Betreuer] Kochenderfer. "Probabilistic Motion Planning for Automated Vehicles / Maximilian Naumann ; Christoph Stiller, Mykel J. Kochenderfer." Karlsruhe : KIT-Bibliothek, 2021. http://d-nb.info/1227451334/34.
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Deveaux, Vincent. "Modèles markoviens partiellement orientés. Approche géométrique des Automates cellulaires probabilistes." Phd thesis, Université de Rouen, 2008. http://tel.archives-ouvertes.fr/tel-00325051.
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Le sujet global de cette thèse est l'étude d'automates cellulaires probabilistes. Elle est divisée en deux grandes parties.Au cours de la première, nous définissons la notion de chaîne partiellement ordonnée qui généralise celle d'automate cellulaire probabiliste. Cette définition se fait par l'intermédiaire de spécification partiellement ordonnée de la même façon que les mesures de Gibbs sont définies à l'aide de spécifications. Nous obtenons des résultats analogues sur l'espace des phases : caractérisation des mesures extrêmes, construction/reconstruction en partant des noyaux sur un seul site, critères d'unicité. Les résultats sont appliqués tout au long du texte à des automates déjà connus.
La deuxième partie est essentiellement vouée à l'étude d'automates cellulaires unidimensionnels à deux voisins et deux états. Nous donnons deux décompositions des configurations spatio-temporelles en flot d'information. Ces flots ont une signification géométrique. De cela nous tirons deux critères d'unicité.
En annexe, nous donnons une démonstration de transition de phase d'un automate cellulaire défini par A. Toom, le modèle NEC. Tout au long du texte, des simulations sont présentées.
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Marcovici, Irène. "Automates cellulaires probabilistes et mesures spécifiques sur des espaces symboliques." Phd thesis, Université Paris-Diderot - Paris VII, 2013. http://tel.archives-ouvertes.fr/tel-00933977.
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Un automate cellulaire probabiliste (ACP) est une chaîne de Markov sur un espace symbolique. Le temps est discret, les cellules évoluent de manière synchrone, et le nouvel état de chaque cellule est choisi de manière aléatoire, indépendamment des autres cellules, selon une distribution déterminée par les états d'un nombre fini de cellules situées dans le voisinage. Les ACP sont utilisés en informatique comme modèle de calcul, ainsi qu'en biologie et en physique. Ils interviennent aussi dans différents contextes en probabilités et en combinatoire. Un ACP est ergodique s'il a une unique mesure invariante qui est attractive. Nous prouvons que pour les AC déterministes, l'ergodicité est équivalente à la nilpotence, ce qui fournit une nouvelle preuve de l'indécidabilité de l'ergodicité pour les ACP. Alors que la mesure invariante d'un AC ergodique est triviale, la mesure invariante d'un ACP ergodique peut être très complexe. Nous proposons un algorithme pour échantillonner parfaitement cette mesure. Nous nous intéressons à des familles spécifiques d'ACP, ayant des mesures de Bernoulli ou des mesures markoviennes invariantes, et étudions les propriétés de leurs diagrammes espace-temps. Nous résolvons le problème de classification de la densité sur les grilles de dimension supérieure ou égale à 2 et sur les arbres. Enfin, nous nous intéressons à d'autres types de problèmes. Nous donnons une caractérisation combinatoire des mesures limites pour des marches aléatoires sur des produits libres de groupes. Nous étudions les mesures d'entropie maximale de sous-décalages de type fini sur les réseaux et sur les arbres. Les ACP interviennent à nouveau dans ce dernier travail.
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Louis, Pierre-Yves. "Ergodicity of PCA : equivalence between spatial and temporal mixing conditions." Universität Potsdam, 2004. http://opus.kobv.de/ubp/volltexte/2006/658/.
Full textAbstract:
For a general attractive Probabilistic Cellular Automata on S-Zd, we prove that the (time-) convergence towards equilibrium of this Markovian parallel dynamics, exponentially fast in the uniform norm, is equivalent to a condition (A). This condition means the exponential decay of the influence from the boundary for the invariant measures of the system restricted to finite boxes. For a class of reversible PCA dynamics on {1,+1}(Zd), wit a naturally associated Gibbsian potential rho, we prove that a (spatial-) weak mixing condition (WM) for rho implies the validity of the assumption (A); thus exponential (time-) ergodicity of these dynamics towards the unique Gibbs measure associated to rho hods. On some particular examples we state that exponential ergodicity holds as soon as there is no phase transition.
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Muhajab, Hanan Nasser. "EXTENDED COUPLED PROBABLISTIC TIMED AUTOMATA FOR MONITORING EATING ACTIVITIES OF ELDERLY PERSON." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1479812233555067.
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Roos, Jason Daniel. "Probabilistic SVM for Open Set Automatic Target Recognition on High Range Resolution Radar Data." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1472248754.
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Gerin, Lucas. "Aspects probabilistes des automates cellulaires et d'autres problèmes en informatique théorique." Phd thesis, Université Henri Poincaré - Nancy I, 2008. http://tel.archives-ouvertes.fr/tel-00589825.
Full textAbstract:
Ce mémoire de thèse est consacré à l'étude de quelques problèmes de probabilités provenant de l'informatique théorique. Dans une première partie, nous étudions un algorithme probabiliste qui compte le nombre de mots différents dans une liste. Nous montrons que l'étude peut se ramener à un problème d'estimation, et qu'en modifiant légèrement cet algorithme, il est d'une certaine manière optimal. La deuxième partie est consacrée à l'étude de plusieurs problèmes de convergences pour des systèmes finis de particules, nous envisageons différents types de passage à une limite infinie. La première famille de systèmes considérés est une classe particulière d'automates cellulaires. En dimension 1, il apparaît des marches aléatoires dont nous caractérisons de façon complète les comportements limites. En dimension 2, sur une grille carrée, nous étudions quelques-un des cas les plus représentatifs. Nous en déterminons le temps moyen de convergence vers une configuration fixe. Enfin, nous étudions un modèle d'urnes avec des boules à deux états. Dans la troisième partie, nous étudions deux problèmes particuliers de marches aléatoires. Ces deux questions sont initialement motivées par l'étude de certains automates cellulaires, mais nous les présentons de façon indépendante. Le premier de ces deux problèmes est l'étude de marches aléatoires sur un tore discret, réfléchies les unes sur les autres. On montre la convergence de ce processus vers une limite brownienne. Nous étudions enfin de façon entièrement combinatoire une famille de marches aléatoires sur un intervalle, biaisées vers le bas. Nous en déterminons le temps moyen de sortie vers le haut.
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LOUIS, Pierre-Yves. "Automates Cellulaires Probabilistes : mesures stationnaires, mesures de Gibbs associées et ergodicité." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2002. http://tel.archives-ouvertes.fr/tel-00002203.
Full textAbstract:
Utilisés dans de nombreux domaines scientifiques, les Automates Cellulaires Probabilistes, usuellement abrégés en PCA, de l'anglais "Probabilistic Cellular Automata", constituent, au sein des dynamiques aléatoires à temps discret, une classe de systèmes infinis de particules, c'est à dire de processus stochastiques markoviens à valeurs dans un espace infini S^G où S désigne un ensemble fini et G est un graphe infini. On considère ici toujours le cas où G=Z^d. La particularité de ces dynamiques est l'évolution en parallèle, ou synchrone, de chacune des coordonnées ou composants élémentaires en interaction. Nous nous intéressons dans un premier temps à l'existence et à l'unicité des mesures stationnaires pour les dynamiques PCA non dégénérées i.e. dont le comportement local n'est jamais déterministe, ainsi qu'à la caractérisation de ces états d'équilibre en tant que mesures gibbsiennes. Nous fondant sur les résultats de Dai Pra, Kozlov, Künsch, Lebowitz, Vasilyev et al., nous précisons, pour la classe des dynamiques PCA réversibles, les relations existant entre les mesures stationnaires, les mesures réversibles et les mesures de Gibbs associées à un potentiel dont le lien avec la dynamique est explicité. Pour une famille paramétrée de dynamiques PCA réversibles, nous démontrons l'existence d'un phénomène de transition de phase et explicitons dans ce cas le comportement de différentes mesures de Gibbs sous l'action de ces dynamiques. En particulier, nous exhibons des mesures de Gibbs non-stationnaires. Dans un second temps, nous étudions l'ergodicité, i.e. la convergence vers l'équilibre des dynamiques PCA qui sont de plus attractives. Nous construisons à cet effet un couplage de ces dynamiques préservant l'ordre stochastique. En nous référant aux travaux de Martinelli et Olivieri pour les dynamiques de Glauber, nous établissons qu'en l'absence de transition de phase, dès que l'unique mesure de Gibbs vérifie une condition de faible mélange, il y a ergodicité et convergence à vitesse exponentielle vers cet unique état d'équilibre, améliorant en cela grandement les critères d'ergodicité pour les PCA existant dans la littérature. Enfin, nous illustrons ces résultats par la réalisation de simulations numériques de certaines des dynamiques réversibles précédemment étudiées, et présentons un algorithme parallèle convergeant vers les mesures de Gibbs extrémales du modèle d'Ising.
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Louis, Pierre-Yves. "Automates cellulaires probabilistes : mesures stationnaires, mesures de Gibbs associées et ergodicité." Lille 1, 2002. https://pepite-depot.univ-lille.fr/LIBRE/Th_Num/2002/50376-2002-12-5-6.pdf.
Full textAbstract:
Les Automates Cellulaires Probabilistes (PCA, de l'anglais Probabilistic Cellular Automata) constituent, au sein des dynamiques aléatoires à temps discret, une classe de processus stochastiques markoviens à valeurs dans un espace infini S exposant G où S désigne un ensemble fini et G est un graphe infini. Ici G=Z exposant d. La particularité de ces dynamiques est l'évolution en parallèle de chacune des coordonnées ou composants élémentaires en interaction. Nous nous intéressons à l'existence et à l'unicité des mesures stationnaires pour les dynamiques PCA non dégénérées dont le comportement local n'est jamais déterministe, ainsi qu'à la caractérisation de ces états d'équilibre en tant que mesures gibbsiennes. Nous précisons, pour la classe des dynamiques PCA réversibles, les relations existant entre les mesures stationnaires, les mesures réversibles et les mesures de Gibbs associées à un potentiel dont le lien avec la dynamique est explicité. Pour une famille paramétrée de dynamiques PCA réversibles, nous démontrons l'existence d'un phénomène de transition de phase, et explicitons dans ce cas le comportement de différentes mesures de Gibbs sous l'action de ces dynamiques. Puis, nous étudions la convergence vers l'équilibre des dynamiques PCA qui sont de plus en plus attractives.
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Morettin, Paolo. "Learning and Reasoning in Hybrid Structured Spaces." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/264203.
Full textAbstract:
Many real world AI applications involve reasoning on both
continuous and discrete variables, while requiring some level of
symbolic reasoning that can provide guarantees on the system's
behaviour. Unfortunately, most of the existing probabilistic models do
not efficiently support hard constraints or they are limited to purely
discrete or continuous scenarios.
Weighted Model Integration (WMI) is a recent and general formalism
that enables probabilistic modeling and inference in hybrid structured
domains. A difference of WMI-based inference algorithms with respect
to most alternatives is that probabilities are computed inside
a structured support involving both logical and algebraic
relationships between variables.
While some progress has been made in the last years and the topic is
increasingly gaining interest from the community, research in this
area is at an early stage.
These aspects motivate the study of hybrid and symbolic probabilistic
models and the development of scalable inference procedures and
effective learning algorithms in these domains.
This PhD Thesis embodies my effort in studying scalable reasoning and
learning techniques in the context of WMI.
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Mahendiran, Aravindan. "Automated Vocabulary Building for Characterizing and Forecasting Elections using Social Media Analytics." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/25430.
Full textAbstract:
Twitter has become a popular data source in the recent decade and garnered a significant amount of attention as a surrogate data source for many important forecasting problems.
Strong correlations have been observed between Twitter indicators and real-world trends spanning elections, stock markets, book sales, and flu outbreaks. A key ingredient to all methods that use Twitter for forecasting is to agree on a domain-specific vocabulary to track the pertinent tweets, which is typically provided by subject matter experts (SMEs).
The language used in Twitter drastically differs from other forms of online discourse,
such as news articles and blogs. It constantly evolves over time as users adopt popular hashtags to express their opinions. Thus, the vocabulary used by forecasting algorithms needs to be dynamic in nature and should capture emerging trends over time. This thesis proposes a novel unsupervised learning algorithm that builds a dynamic vocabulary using Probabilistic Soft Logic (PSL), a framework for probabilistic reasoning over relational domains. Using eight presidential elections from Latin America, we show how our query expansion methodology improves the performance of traditional election forecasting algorithms. Through this approach we demonstrate how we can achieve close to a two-fold increase in the number of tweets retrieved for predictions and a 36.90% reduction in prediction error.Master of Science