Relevant bibliographies by topics / Probabilistic analysi

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  2. Dissertations / Theses
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Academic literature on the topic 'Probabilistic analysi'

Author: Grafiati
Published: 9 March 2023

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Journal articles on the topic "Probabilistic analysi"

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De la Sen, M. "On Probabilistic Alpha-Fuzzy Fixed Points and Related Convergence Results in Probabilistic Metric and Menger Spaces under Some Pompeiu-Hausdorff-Like Probabilistic Contractive Conditions." Journal of Function Spaces 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/213174.

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In the framework of complete probabilistic metric spaces and, in particular, in probabilistic Menger spaces, this paper investigates some relevant properties of convergence of sequences to probabilisticα-fuzzy fixed points under some types of probabilistic contractive conditions.
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Riveros, Bruno, Mariana Rosim, Gabriel Pedro, Rosa Lucchetta, and Marcelo Nita. "Análise de custo-efetividade e a análise de sensibilidade, um roteiro para execução de uma abordagem probabilística: Introdução à análise de sensibilidade probabilística (Parte I)." Jornal de Assistência Farmacêutica e Farmacoeconomia 1, no. 2 (December 2016): 33–38. http://dx.doi.org/10.22563/2525-7323.2016.v1.n2.p.33-38.

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Pavese, Carlotta. "Probabilistic Knowledge in Action." Analysis 80, no. 2 (April 1, 2020): 342–56. http://dx.doi.org/10.1093/analys/anz094.

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Irzik, G. "Armstrong's account of probabilistic laws." Analysis 51, no. 4 (October 1, 1991): 214–17. http://dx.doi.org/10.1093/analys/51.4.214.

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Fitelson, B. "A probabilistic theory of coherence." Analysis 63, no. 3 (July 1, 2003): 194–99. http://dx.doi.org/10.1093/analys/63.3.194.

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Tsujimoto, Kazuki, and Toshiaki Omori. "Switching Probabilistic Slow Feature Analysis for Time Series Data." International Journal of Machine Learning and Computing 10, no. 6 (December 2020): 740–45. http://dx.doi.org/10.18178/ijmlc.2020.10.6.999.

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Chase, J. "The non-probabilistic two envelope paradox." Analysis 62, no. 2 (April 1, 2002): 157–60. http://dx.doi.org/10.1093/analys/62.2.157.

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Flandoli, Franco, and Marco Romito. "Probabilistic analysis of singularities for the 3D Navier-Stokes equations." Mathematica Bohemica 127, no. 2 (2002): 211–18. http://dx.doi.org/10.21136/mb.2002.134166.

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LIU, Jinlin, and Changhong PENG. "ICONE23-1839 AN OVERVIEW-PROBABILISTIC SAFETY ANALYSIS FOR RESEARCH REACTORS." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2015.23 (2015): _ICONE23–1—_ICONE23–1. http://dx.doi.org/10.1299/jsmeicone.2015.23._icone23-1_397.

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Friederichs, Petra, Martin Göber, Sabrina Bentzien, Anne Lenz, and Rebekka Krampitz. "A probabilistic analysis of wind gusts using extreme value statistics." Meteorologische Zeitschrift 18, no. 6 (December 1, 2009): 615–29. http://dx.doi.org/10.1127/0941-2948/2009/0413.

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Dissertations / Theses on the topic "Probabilistic analysi"

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POZZI, FEDERICO ALBERTO. "Probabilistic Relational Models for Sentiment Analysis in Social Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/65709.

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The huge amount of textual data on theWeb has grown in the last few years rapidly creating unique contents of massive dimensions that constitutes fertile ground for Sentiment Analysis. In particular, social networks represents an emerging challenging sector where the natural language expressions of people can be easily reported through short but meaningful text messages. This unprecedented contents of huge dimensions need to be efficiently and effectively analyzed to create actionable knowledge for decision making processes. A key information that can be grasped from social environments relates to the polarity of text messages, i. e. the sentiment (positive, negative or neutral) that the messages convey. However, most of the works regarding polarity classification usually consider text as unique information to infer sentiment, do not taking into account that social networks are actually networked environments. A representation of real world data where instances are considered as homogeneous, independent and identically distributed (i.i.d.) leads us to a substantial loss of information and to the introduction of a statistical bias. For this reason, the combination of content and relationships is a core task of the recent literature on Sentiment Analysis, where friendships are usually investigated to model the principle of homophily (a contact among similar people occurs at a higher rate than among dissimilar people). However, paired with the assumption of homophily, constructuralism explains how social relationships evolve via dynamic and continuous interactions as the knowledge and behavior that two actors share increase. Considering the similarity among users on the basis of constructuralism appears to be a much more powerful force than interpersonal influence within the friendship network. As first contribution, this Ph.D. thesis proposes Approval Network as a novel graph representation to jointly model homophily and constructuralism, which is intended to better represent the contagion on social networks. Starting from the classical state-of-the-art methodologies where only text is used to infer the polarity of social networks messages, this thesis presents novel Probabilistic Relational Models on user, document and aspect-level which integrate the structural information to improve classification performance. The integration is particularly useful when textual features do not provide sufficient or explicit information to infer sentiment (e. g., I agree!). The experimental investigations reveal that incorporating network information through approval relations can lead to statistically significant improvements over the performance of complex learning approaches based only on textual features.
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Crotta, M. "PROBABILISTIC MODELLING IN FOOD SAFETY: A SCIENCE-BASED APPROACH FOR POLICY DECISIONS." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/339138.

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This thesis deals with use of qualitative and quantitative probabilistic models for the animal-derived food safety management. Four unrelated models are presented: three quantitative and one qualitative. Two of the quantitative models concern the risk posed by pathogens in raw milk, in the first study, a probabilistic approach for the inclusion of the variability and the uncertainty in the consumers’ habits and the bacterial pathogenic potential is proposed while the second study, demonstrate how the overlook of the relationship between the storage time and temperature has led to overestimated results in raw milk-related models published so far and an equation to address the issue is provided. In the third study, quantitative modelling techniques are used to simulate the dynamics underlying the spread of Campylobacter in broiler flocks and quantify the potential effects that different on-farm mitigation strategies or management measures have on the microbial load in the intestine of infected birds at the end of the rearing period. In the qualitative study, a general approach for the estimation of the likelihoods of introduction of live parasites in aquaculture implants and the commercialization of infested product is outlined by using the example of Anisakids in farmed Atlantic salmon.
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SCOZZESE, FABRIZIO. "AN EFFICIENT PROBABILISTIC FRAMEWORK FOR SEISMIC RISK ANALYSIS OF STRUCTURAL SYSTEMS EQUIPPED WITH LINEAR AND NONLINEAR VISCOUS DAMPERS." Doctoral thesis, Università degli Studi di Camerino, 2018. http://hdl.handle.net/11581/429547.

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Seismic passive protection with supplemental damping devices represents an efficient strategy to produce resilient structural systems with improved seismic performances and notably reduced post-earthquake consequences. Such strategy offers indeed several advantages with respect to the ordinary seismic design philosophy: structural damages are prevented; the safety of the occupants is ensured and the system remains operational both during and right after the earthquake; no major retrofit interventions are needed but only a post-earthquake inspection (and if necessary, replacement) of dissipation devices is required; a noticeable reduction of both direct and indirect outlays is achieved. However, structural systems equipped with seismic control devices (dampers) may show potentially limited robustness, since an unexpected early disruption on the dampers may lead to a progressive collapse of the actually non-ductile system. Although the most advanced international seismic codes acknowledge this issue and require dampers to have higher safety margins against the failure, they only provide simplified approaches to cope with the problem, often consisting of general demand amplification rules which are not tailored on the actual needs of different device typologies and which lead to reliability levels not explicitly declared. The research activity carried out within this Thesis stems from the need to fill the gaps still present in the international regulatory framework, and respond to the scarcity of specific probabilistic studies geared to characterize and understand the probabilistic seismic response of such systems up to very low failure probabilities. In particular, as a first step towards this goal, the present work aims at addressing the issue of the seismic risk of structures with fluid viscous dampers, a simple and widely used class of dissipation devices. A robust probabilistic framework has been defined for the purposes of the present work, made up of the combination of an advanced probabilistic tool for solving reliability problems, consisting of Subset Simulation (with Markov chain Monte Carlo and Metropolis-like algorithms), and a stochastic ground motion model for statistical seismic hazard characterization. The seismic performance of the system is described by means of demand hazard curves, providing the mean annual frequency of exceeding any specified threshold demand value for all the relevant global and local Engineering Demand Parameters (EDPs). A wide range of performance levels is monitored, encompassing the serviceability conditions, the ultimate limit states, up to very rare performance demand levels (with mean annual frequency of exceedance around 10-6) at which the seismic reliability shall be checked in order to confer the system an adequate level of safety margins against seismic events rarer than the design one. Some original contributions regarding the methodological approaches have been obtained by an efficient combination of the common conditional probabilistic methods (i.e., multiple-stripe and cloud analysis) with a stochastic earthquake model, in which subset simulation is exploited for efficiently generate both the seismic hazard curve and the ground motion samples for structural analysis purposes. The accuracy of the proposed strategy is assessed by comparing the achieved seismic risk estimates with those provided via Subset Simulation, the latter being assumed as reference reliability method. Furthermore, a reliability-based optimization method is proposed as powerful tool for investigating upon the seismic risk sensitivity to variable model parameters. Such method proves to be particularly useful when a proper statistical characterization of the model parameters is not available. The proposed probabilistic framework is applied to a set of single-degree-of-freedom damped models to carry out an extensive parametric analysis, and to a multi-story steel building with linear and nonlinear viscous dampers for the aims of a deeper investigation. The influence of viscous dampers nonlinearity level on the seismic risk of such systems is investigated. The variability of viscous constitutive parameters due to the tolerance allowed in devices’ quality control and production tests is also accounted for, and the consequential effects on the seismic performances are evaluated. The reliability of simplified approaches proposed by the main international seismic codes for dampers design is assessed, the main regulatory gaps are highlighted and proposals for improvement are given as well. Results from this whole probabilistic investigation contribute to the development of more reliable design procedures for seismic passive protection strategies.
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Tagliaferri, Lorenza. "Probabilistic Envelope Curves for Extreme Rainfall Events - Curve Inviluppo Probabilistiche per Precipitazioni Estreme." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amslaurea.unibo.it/99/.

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A regional envelope curve (REC) of flood flows summarises the current bound on our experience of extreme floods in a region. RECs are available for most regions of the world. Recent scientific papers introduced a probabilistic interpretation of these curves and formulated an empirical estimator of the recurrence interval T associated with a REC, which, in principle, enables us to use RECs for design purposes in ungauged basins. The main aim of this work is twofold. First, it extends the REC concept to extreme rainstorm events by introducing the Depth-Duration Envelope Curves (DDEC), which are defined as the regional upper bound on all the record rainfall depths at present for various rainfall duration. Second, it adapts the probabilistic interpretation proposed for RECs to DDECs and it assesses the suitability of these curves for estimating the T-year rainfall event associated with a given duration and large T values. Probabilistic DDECs are complementary to regional frequency analysis of rainstorms and their utilization in combination with a suitable rainfall-runoff model can provide useful indications on the magnitude of extreme floods for gauged and ungauged basins. The study focuses on two different national datasets, the peak over threshold (POT) series of rainfall depths with duration 30 min., 1, 3, 9 and 24 hrs. obtained for 700 Austrian raingauges and the Annual Maximum Series (AMS) of rainfall depths with duration spanning from 5 min. to 24 hrs. collected at 220 raingauges located in northern-central Italy. The estimation of the recurrence interval of DDEC requires the quantification of the equivalent number of independent data which, in turn, is a function of the cross-correlation among sequences. While the quantification and modelling of intersite dependence is a straightforward task for AMS series, it may be cumbersome for POT series. This paper proposes a possible approach to address this problem.
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Saad, Feras Ahmad Khaled. "Probabilistic data analysis with probabilistic programming." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/113164.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 48-50).
Probabilistic techniques are central to data analysis, but dierent approaches can be challenging to apply, combine, and compare. This thesis introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler's Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.
by Feras Ahmad Khaled Saad.
M. Eng.
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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 pondérés
Markov 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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Baier, Christel, Benjamin Engel, Sascha Klüppelholz, Steffen Märcker, Hendrik Tews, and Marcus Völp. "A Probabilistic Quantitative Analysis of Probabilistic-Write/Copy-Select." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-129917.

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Probabilistic-Write/Copy-Select (PWCS) is a novel synchronization scheme suggested by Nicholas Mc Guire which avoids expensive atomic operations for synchronizing access to shared objects. Instead, PWCS makes inconsistencies detectable and recoverable. It builds on the assumption that, for typical workloads, the probability for data races is very small. Mc Guire describes PWCS for multiple readers but only one writer of a shared data structure. In this paper, we report on the formal analysis of the PWCS protocol using a continuous-time Markov chain model and probabilistic model checking techniques. Besides the original PWCS protocol, we also considered a variant with multiple writers. The results were obtained by the model checker PRISM and served to identify scenarios in which the use of the PWCS protocol is justified by guarantees on the probability of data races. Moreover, the analysis showed several other quantitative properties of the PWCS protocol.
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Munch, Mélanie. "Améliorer le raisonnement dans l'incertain en combinant les modèles relationnels probabilistes et la connaissance experte." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASB011.

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Cette thèse se concentre sur l'intégration des connaissances d'experts pour améliorer le raisonnement dans l'incertitude. Notre objectif est de guider l'apprentissage des relations probabilistes avec les connaissances d'experts pour des domaines décrits par les ontologies.Pour ce faire, nous proposons de coupler des bases de connaissances (BC) et une extension orientée objet des réseaux bayésiens, les modèles relationnels probabilistes (PRM). Notre objectif est de compléter l'apprentissage statistique par des connaissances expertes afin d'apprendre un modèle aussi proche que possible de la réalité et de l'analyser quantitativement (avec des relations probabilistes) et qualitativement (avec la découverte causale). Nous avons développé trois algorithmes à travers trois approches distinctes, dont les principales différences résident dans leur automatisation et l'intégration (ou non) de la supervision d'experts humains.L'originalité de notre travail est la combinaison de deux philosophies opposées : alors que l'approche bayésienne privilégie l'analyse statistique des données fournies pour raisonner avec, l'approche ontologique est basée sur la modélisation de la connaissance experte pour représenter un domaine. La combinaison de la force des deux permet d'améliorer à la fois le raisonnement dans l'incertitude et la connaissance experte
This thesis focuses on integrating expert knowledge to enhance reasoning under uncertainty. Our goal is to guide the probabilistic relations’ learning with expert knowledge for domains described by ontologies.To do so we propose to couple knowledge bases (KBs) and an oriented-object extension of Bayesian networks, the probabilistic relational models (PRMs). Our aim is to complement the statistical learning with expert knowledge in order to learn a model as close as possible to the reality and analyze it quantitatively (with probabilistic relations) and qualitatively (with causal discovery). We developped three algorithms throught three distinct approaches, whose main differences lie in their automatisation and the integration (or not) of human expert supervision.The originality of our work is the combination of two broadly opposed philosophies: while the Bayesian approach favors the statistical analysis of the given data in order to reason with it, the ontological approach is based on the modelization of expert knowledge to represent a domain. Combining the strenght of the two allows to improve both the reasoning under uncertainty and the expert knowledge
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Echard, Benjamin. "Assessment by kriging of the reliability of structures subjected to fatigue stress." Thesis, Clermont-Ferrand 2, 2012. http://www.theses.fr/2012CLF22269/document.

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Les méthodes traditionnelles de dimensionnement à la fatigue s’appuient sur l’utilisation de coefficients dits de “sécurité” dans le but d’assurer l’intégrité de la structure en couvrant les incertitudes inhérentes à la fatigue. Ces méthodes de l’ingénieur ont le mérite d’être simples d’application et de donner des solutions heureusement satisfaisantes du point de vue de la sécurité. Toutefois, elles ne permettent pas au concepteur de connaître la véritable marge de sécurité de la structure et l’influence des différents paramètres de conception sur la fiabilité. Les approches probabilistes sont envisagées dans cette thèse afin d’acquérir ces informations essentielles pour un dimensionnement optimal de la structure vis-à-vis de la fatigue. Une approche générale pour l’analyse probabiliste en fatigue est proposée dans ce manuscrit. Elle s’appuie sur la modélisation des incertitudes (chargement, propriétés du matériau, géométrie, courbe de fatigue) et vise à quantifier le niveau de fiabilité de la structure étudiée pour un scénario de défaillance en fatigue. Les méthodes classiques de fiabilité nécessitent un nombre important d’évaluations du modèle mécanique de la structure et ne sont donc pas envisageables lorsque le calcul du modèle est coûteux en temps. Une famille de méthodes appelée AK-RM (Active learning and Kriging-based Reliability Methods) est précisément proposée dans ces travaux de thèse afin de résoudre le problème de fiabilité avec un minimum d’évaluations du modèle mécanique. L’approche générale est appliquée à deux cas-tests fournis par SNECMA dans le cadre du projet ANR APPRoFi
Traditional procedures for designing structures against fatigue are grounded upon the use of so-called safety factors in an attempt to ensure structural integrity while masking the uncertainties inherent to fatigue. These engineering methods are simple to use and fortunately, they give satisfactory solutions with regard to safety. However, they do not provide the designer with the structure’s safety margin as well as the influence of each design parameter on reliability. Probabilistic approaches are considered in this thesis in order to acquire this information, which is essential for an optimal design against fatigue. A general approach for probabilistic analysis in fatigue is proposed in this manuscript. It relies on the modelling of the uncertainties (load, material properties, geometry, and fatigue curve), and aims at assessing the reliability level of the studied structure in the case of a fatigue failure scenario. Classical reliability methods require a large number of calls to the mechanical model of the structure and are thus not applicable when the model evaluation is time-demanding. A family of methods named AK-RM (Active learning and Kriging-based Reliability methods) is proposed in this research work in order to solve the reliability problem with a minimum number of mechanical model evaluations. The general approach is applied to two case studies submitted by SNECMA in the frame of the ANR project APPRoFi
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Kassa, Negede Abate. "Probabilistic safety analysis of dams." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-60843.

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Successful dam design endeavor involves generating technical solutions that can meet intended functional objectives and choosing the best one among the alternative technical solutions. The process of choosing the best among the alternative technical solutions depends on evaluation of design conformance with technical specifications and reliability standards (such as capacity, environmental, safety, social, political etc pecifications). The process also involves evaluation on whether an optimal balance is set between safety and economy. The process of evaluating alternative design solutions requires generating a quantitative expression for lifetime performance and safety. An objective and numerical evaluation of lifetime performance and safety of dams is an essential but complex undertaking. Its domain involves much uncertainty (uncertainty in loads, hazards, strength parameters, boundary conditions, models and dam failure consequences) all of which should be characterized. Arguably uncertainty models and risk analysis provide the most complete characterization of dam performance and safety issues. Risk is a combined measure of the probability and severity of an adverse effect (functional and/or structural failure), and is often estimated by the product of the probability of the adverse event occurring and the expected consequences. Thus, risk analysis requires (1) determination of failure probabilities. (2) probabilistic estimation of consequences. Nonetheless, there is no adequately demonstrated, satisfactorily comprehensive and precise method for explicit treatment and integration of all uncertainties in variables of dam design and risk analysis. Therefore, there is a need for evaluating existing uncertainty models for their applicability, to see knowledge and realization gaps, to drive or adopt new approaches and tools and to adequately demonstrate their practicability by using real life case studies. This is required not only for hopefully improving the performance and safety evaluation process accuracy but also for getting better acceptance of the probabilistic approaches by those who took deterministic design based research and engineering practices as their life time career. These problems have motivated the initiation of this research. In this research the following have been accomplished: (1) Identified various ways of analyzing and representing uncertainty in dam design parameters pertinent to three dominant dam failure causes (sliding, overtopping and seepage), and tested a suite of stochastic models capable of capturing design parameters uncertainty to better facilitate evaluation of failure probabilities; (2) Studied three classical stochastic models: Monte Carlo Simulation Method (MCSM), First Order Second Moment (FOSM) and Second Order Second Moment (SOSM), and applied them for modeling dam performance and for evaluating failure probabilities in line with the above mentioned dominant dam failure causes; (3) Presented an exact new for the purpose analytical method of transforming design parameters distributions to a distribution representing dam performance (Analytical Solution for finding Derived Distributions (ASDD) method). Laid out proves of its basic principles, prepared a generic implementation architecture and demonstrated its applicability for the three failure modes using a real life case study data; (4) Presented a multitude of tailor-made reliability equations and solution procedures that will enable the implementations of the above stochastic and analytical methods for failure probability evaluation; (5) Implemented the stochastic and analytical methods using real life data pertinent to the three failure mechanisms from Tendaho Dam, Ethiopia. Compared the performance of the various stochastic and analytical methods with each other and with the classical deterministic design approach; and (6) Provided solution procedures, implementation architectures, and Mathematica 5.2, Crystal Ball 7 and spreadsheet based tools for doing the above mentioned analysis. The results indicate that: (1) The proposed approaches provide a valid set of procedures, internally consistent logic and produce more realistic solutions. Using the approaches engineers could design dams to meet a quantified level of performance (volume of failure) and could set a balance between safety and economy; (2) The research is assumed to bridge the gap between the available probability theories in one hand and the suffering distribution problems in dam safety evaluation on the other; (3) Out of the suite of stochastic approaches studied the ASDD method out perform the classical methods (MCSM, FOSM and SOSM methods) by its theoretical foundation, accuracy and reproducibility. However, when compared with deterministic approach, each of the stochastic approaches provides valid set of procedures, consistent logic and they gave more realistic solution. Nonetheless, it is good practice to compare results from the proposed probabilistic approaches; (4) The different tailor-made reliability equations and solution approaches followed are proved to work for stochastic safety evaluation of dams; and (5) The research drawn from some important conclusions and lessons, in relation to stochastic safety analysis of dams against the three dominant failure mechanisms, are. The end result of the study should provide dam engineers and decision makers with perspectives, methodologies, techniques and tools that help them better understand dam safety related issues and enable them to conduct quantitative safety analysis and thus make intelligent dam design, upgrading and rehabilitation decisions.
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Books on the topic "Probabilistic analysi"

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Hynes, Mary Ellen. Probabilistic liquefaction analysis. Washington, DC: Division of Engineering Technology, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1999.

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Hynes, M. E. Probabilistic liquefaction analysis. Washington, D.C: U.S. Nuclear Regulatory Commission, 1990.

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Hofri, Micha. Probabilistic Analysis of Algorithms. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-4800-2.

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Probabilistic techniques in analysis. New York: Springer-Verlag, 1995.

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National Research Council (U.S.). Panel on Seismic Hazard Analysis. Probabilistic seismic hazard analysis. Washington, D.C: National Academy Press, 1988.

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Alon, Noga. The probabilistic method. 3rd ed. New York, NY: John Wiley, 2008.

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Alon, Noga. The probabilistic method. New York: Wiley, 1992.

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H, Spencer Joel, ed. The probabilistic method. Hoboken, New Jersey: John Wiley & Sons, Inc., 2016.

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Alon, Noga. The Probabilistic Method. New York: John Wiley & Sons, Ltd., 2005.

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Alon, Noga. The probabilistic method. 3rd ed. New York, NY: John Wiley, 2008.

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Book chapters on the topic "Probabilistic analysi"

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Reidys, Christian. "Probabilistic Analysis." In Combinatorial Computational Biology of RNA, 143–86. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-76731-4_5.

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Snapp, Robert R. "Probabilistic Analysis." In Encyclopedia of Social Network Analysis and Mining, 1–28. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_155-1.

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Maxim, Dorin, Liliana Cucu-Grosjean, and Robert I. Davis. "Probabilistic Analysis." In Handbook of Real-Time Computing, 1–23. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-4585-87-3_9-1.

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Snapp, Robert R. "Probabilistic Analysis." In Encyclopedia of Social Network Analysis and Mining, 1362–88. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_155.

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Maxim, Dorin, Liliana Cucu-Grosjean, and Robert I. Davis. "Probabilistic Analysis." In Handbook of Real-Time Computing, 323–46. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-287-251-7_9.

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Bürgisser, Peter, and Felipe Cucker. "Probabilistic Analysis." In Grundlehren der mathematischen Wissenschaften, 21–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38896-5_2.

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Snapp, Robert R. "Probabilistic Analysis." In Encyclopedia of Social Network Analysis and Mining, 1866–92. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_155.

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Kingston, John, Robert Nertney, Rudolf Frei, Philippe Schallier, and Floor Koornneef. "Barrier Analysis Analysed in MORT Perspective." In Probabilistic Safety Assessment and Management, 364–69. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_59.

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Huang, Xiaoxia. "Probabilistic Portfolio Selection." In Portfolio Analysis, 11–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11214-0_2.

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Larsen, Rasmus, and Klaus Baggesen Hilger. "Probabilistic Generative Modelling." In Image Analysis, 861–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_114.

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Conference papers on the topic "Probabilistic analysi"

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Ramanath, Vinay, and Gene E. Wiggs. "DACE Based Probabilistic Optimization of Mechanical Components." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-91024.

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Application of DACE (Design and Analysis of Computer Experiments) methods for probabilistic design space exploration and optimization to the design of a mechanical component is demonstrated. The key part of the paper is focused on the problem formulation and process flow for performing a probabilistic optimization. The authors have shown that for computationally intensive problems, probabilistic optimization can be carried out efficiently within a DACE framework. For problems that are not costly to compute, direct probabilistic optimization can be carried out by the efficient integration of probabilistic analysis and global optimization (such as Genetic Algorithms). The strategy in the paper proves to be especially beneficial for those organizations that are reluctant to move to probabilistic methods and also for the current practitioners of probabilistics. The methodology is illustrated with examples from both simple and computationally intensive engineering problems.
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Parajuli, H. Ram, J. Kiyono, H. Taniguchi, K. Toki, and P. Nath Maskey. "Probabilistic seismic hazard assessment for Nepal." In RISK ANALYSIS 2010. Southampton, UK: WIT Press, 2010. http://dx.doi.org/10.2495/risk100351.

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Carvalho, E., J. Cruz, P. Barahona, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Probabilistic Reasoning with Continuous Constraints." In Numerical Analysis and Applied Mathematics. AIP, 2007. http://dx.doi.org/10.1063/1.2790083.

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Sallaberry, Cédric J., Robert E. Kurth, Frederick W. Brust, and Elizabeth A. Kurth. "Proposed Approach of Scenario Analysis Using a Probabilistic Code." In ASME 2017 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/pvp2017-65989.

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The following paper presents a global methodology to analyze the set of results generated by a probabilistic analysis. The approach regroups traditional analyses such as Sensitivity Analysis (SA), Uncertainty Analysis (UA), and stability analysis as well as sensitivity studies (both deterministic and probabilistic) with enhanced sampling techniques (double loop to separate aleatory from epistemic uncertainty, importance sampling, adaptive sampling) in an incremental set of steps, with the goal to give the analyst and decision maker the most comprehensive and defensible collection of results. An example using the xLPR code and a selected scenario is used to illustrate each step of the approach.
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Chang, Kuang-Hua, Xiaoming Yu, and Kyung Choi. "Probabilistic structural stability prediction." In 6th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-4064.

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Shin, Youngwon. "Improving Probabilistic Damage Tolerance Analysis for Inspection Optimization: Possibilistic-Probabilistic Approach." In 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-6050.

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Wang, C., W. Gao, and S. Tangaramvong. "Hybrid Probabilistic and Non-Probabilistic Analysis of Structures with Mixed Uncertainties." In Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA). Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413609.220.

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Tavakoli, Yashar, H. Haj Seyyed Javadi, and Hossein Erfani. "A Probabilistic Analysis for Greedy Paths." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2008. American Institute of Physics, 2008. http://dx.doi.org/10.1063/1.2990983.

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Afshin Abdollahi. "Probabilistic decision diagrams for exact probabilistic analysis." In 2007 IEEE/ACM International Conference on Computer-Aided Design. IEEE, 2007. http://dx.doi.org/10.1109/iccad.2007.4397276.

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Bhimanadam, V. R., and F. J. Blom. "Probabilistic PTS Analysis." In ASME 2016 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/pvp2016-63112.

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This paper describes the effect of variability of fracture toughness of nuclear pressure vessels during a PTS event. The model used in this paper is based on the NESC-1 experiment. To determine the behavior of the surface breaking defect NRG performed three dimensional finite element calculations and subsequently extended these calculations to the probabilistic calculations. Three-dimensional finite-element model of the cladded cylinder was generated using ANSYS with semi-elliptical surface crack having a crack depth of 75 mm and a crack length of 205 mm. The cylinder specimen was subjected to thermal-shock and centrifugal loading conditions and analyzed with a themo-elastic-plastic material model and subsequently determined the fracture mechanics parameters (J and K) along the elliptical crack front as a function of time and temperature. The determined stress intensity factor K has been compared with the available cleavage fracture toughness (KJC) data with 50% fracture probability which has been obtained from the Master Curve according to BS7910. The comparison has been performed for the locations in the base metal as well as the locations in the heat affected zone. Deterministic analysis has been extended to probabilistic analysis to calculate the failure probability for the crack initiation at the locations in the base metal as well as the locations in the heat affected zone along the crack front by considering probabilistic distributions from Master Curve and FAVOR. Master Curve analysis through the ASME code case N-629 has been applied to the material. Results obtained from these two methods have been compared and also the results are used to compare the inherent safety factors in the deterministic analysis using RTNDT and Master Curve.
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Reports on the topic "Probabilistic analysi"

1

Benson, William E., Jr Berg, and Joseph W. Probabilistic Seismic Hazard Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada203074.

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Cohen, Paul R. Probabilistic, Dynamic Analysis of Plans. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada422223.

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Darwiche, Adnan. Probabilistic Sensitivity Analysis for Situation Awareness. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada484629.

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Hansen, Jeffery. Probabilistic Analysis of Time Sensitive Systems. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada610981.

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Blakely, Scott. Probabilistic Analysis for Reliable Logic Circuits. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1859.

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Cheverton, R. D., and T. L. Dickson. HFIR vessel probabilistic fracture mechanics analysis. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/654200.

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Zio, Enrico, and Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, May 2012. http://dx.doi.org/10.57071/155chr.

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This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides: a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techniques for uncertainty representation; a description of the different steps involved in a Probabilistic Risk Assessment (PRA) or Quantitative Risk Assessment (QRA), and an analysis of the types of uncertainty that can affect each of these steps; annexes giving an overview of a number of tools used during probabilistic risk assessment, including the HAZID technique, fault trees and event tree analysis.
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Franco, John. Probabilistic Analysis of Algorithms for NP-Complete Problems. Fort Belvoir, VA: Defense Technical Information Center, October 1986. http://dx.doi.org/10.21236/ada179537.

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Blumenthal, Saul, and Prem Goel. Fatigue Crack Propagation: Probabilistic Modeling and Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada195885.

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Seitz, R. PROBABILISTIC SENSITIVITY AND UNCERTAINTY ANALYSIS WORKSHOP SUMMARY REPORT. Office of Scientific and Technical Information (OSTI), June 2008. http://dx.doi.org/10.2172/933167.

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