Relevant bibliographies by topics / Probabilistic representation

Academic literature on the topic 'Probabilistic representation'

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Published: 4 June 2021
Last updated: 1 June 2024

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Journal articles on the topic "Probabilistic representation"

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JAEGER, MANFRED. "PROBABILISTIC DECISION GRAPHS — COMBINING VERIFICATION AND AI TECHNIQUES FOR PROBABILISTIC INFERENCE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, supp01 (January 2004): 19–42. http://dx.doi.org/10.1142/s0218488504002564.

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We adopt probabilistic decision graphs developed in the field of automated verification as a tool for probabilistic model representation and inference. We show that probabilistic inference has linear time complexity in the size of the probabilistic decision graph, that the smallest probabilistic decision graph for a given distribution is at most as large as the smallest junction tree for the same distribution, and that in some cases it can in fact be much smaller. Behind these very promising features of probabilistic decision graphs lies the fact that they integrate into a single coherent framework a number of representational and algorithmic optimizations developed for Bayesian networks (use of hidden variables, context-specific independence, structured representation of conditional probability tables).
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Al-Najjar, Nabil I., Ramon Casadesus-Masanell, and Emre Ozdenoren. "Probabilistic representation of complexity." Journal of Economic Theory 111, no. 1 (July 2003): 49–87. http://dx.doi.org/10.1016/s0022-0531(03)00075-9.

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Giannarakis, Nick, Alexandra Silva, and David Walker. "ProbNV: probabilistic verification of network control planes." Proceedings of the ACM on Programming Languages 5, ICFP (August 22, 2021): 1–30. http://dx.doi.org/10.1145/3473595.

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ProbNV is a new framework for probabilistic network control plane verification that strikes a balance between generality and scalability. ProbNV is general enough to encode a wide range of features from the most common protocols (eBGP and OSPF) and yet scalable enough to handle challenging properties, such as probabilistic all-failures analysis of medium-sized networks with 100-200 devices. When there are a small, bounded number of failures, networks with up to 500 devices may be verified in seconds. ProbNV operates by translating raw CISCO configurations into a probabilistic and functional programming language designed for network verification. This language comes equipped with a novel type system that characterizes the sort of representation to be used for each data structure: concrete for the usual representation of values; symbolic for a BDD-based representation of sets of values; and multi-value for an MTBDD-based representation of values that depend upon symbolics. Careful use of these varying representations speeds execution of symbolic simulation of network models. The MTBDD-based representations are also used to calculate probabilistic properties of network models once symbolic simulation is complete. We implement the language and evaluate its performance on benchmarks constructed from real network topologies and synthesized routing policies.
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Lindstr�m, Sten, and Wlodzimierz Rabinowicz. "On probabilistic representation of non-probabilistic belief revision." Journal of Philosophical Logic 18, no. 1 (February 1989): 69–101. http://dx.doi.org/10.1007/bf00296175.

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Konidaris, George, Leslie Pack Kaelbling, and Tomas Lozano-Perez. "From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning." Journal of Artificial Intelligence Research 61 (January 31, 2018): 215–89. http://dx.doi.org/10.1613/jair.5575.

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We consider the problem of constructing abstract representations for planning in high-dimensional, continuous environments. We assume an agent equipped with a collection of high-level actions, and construct representations provably capable of evaluating plans composed of sequences of those actions. We first consider the deterministic planning case, and show that the relevant computation involves set operations performed over sets of states. We define the specific collection of sets that is necessary and sufficient for planning, and use them to construct a grounded abstract symbolic representation that is provably suitable for deterministic planning. The resulting representation can be expressed in PDDL, a canonical high-level planning domain language; we construct such a representation for the Playroom domain and solve it in milliseconds using an off-the-shelf planner. We then consider probabilistic planning, which we show requires generalizing from sets of states to distributions over states. We identify the specific distributions required for planning, and use them to construct a grounded abstract symbolic representation that correctly estimates the expected reward and probability of success of any plan. In addition, we show that learning the relevant probability distributions corresponds to specific instances of probabilistic density estimation and probabilistic classification. We construct an agent that autonomously learns the correct abstract representation of a computer game domain, and rapidly solves it. Finally, we apply these techniques to create a physical robot system that autonomously learns its own symbolic representation of a mobile manipulation task directly from sensorimotor data---point clouds, map locations, and joint angles---and then plans using that representation. Together, these results establish a principled link between high-level actions and abstract representations, a concrete theoretical foundation for constructing abstract representations with provable properties, and a practical mechanism for autonomously learning abstract high-level representations.
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Halpern, J. Y., and D. Koller. "Representation Dependence in Probabilistic Inference." Journal of Artificial Intelligence Research 21 (March 1, 2004): 319–56. http://dx.doi.org/10.1613/jair.1292.

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Non-deductive reasoning systems are often representation dependent: representing the same situation in two different ways may cause such a system to return two different answers. Some have viewed this as a significant problem. For example, the principle of maximum entropyhas been subjected to much criticism due to its representation dependence. There has, however, been almost no work investigating representation dependence. In this paper, we formalize this notion and show that it is not a problem specific to maximum entropy. In fact, we show that any representation-independent probabilistic inference procedure that ignores irrelevant information is essentially entailment, in a precise sense. Moreover, we show that representation independence is incompatible with even a weak default assumption of independence. We then show that invariance under a restricted class of representation changes can form a reasonable compromise between representation independence and other desiderata, and provide a construction of a family of inference procedures that provides such restricted representation independence, using relative entropy.
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Karpati, A., P. Adam, and J. Janszky. "Quantum operations in probabilistic representation." Physica Scripta T135 (July 2009): 014054. http://dx.doi.org/10.1088/0031-8949/2009/t135/014054.

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Barber, M. J., J. W. Clark, and C. H. Anderson. "Neural Representation of Probabilistic Information." Neural Computation 15, no. 8 (August 1, 2003): 1843–64. http://dx.doi.org/10.1162/08997660360675062.

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It has been proposed that populations of neurons process information in terms of probability density functions (PDFs) of analog variables. Such analog variables range, for example, from target luminance and depth on the sensory interface to eye position and joint angles on the motor output side. The requirement that analog variables must be processed leads inevitably to a probabilistic description, while the limited precision and lifetime of the neuronal processing units lead naturally to a population representation of information. We show how a time-dependent probability densityρ(x; t) over variable x, residing in a specified function space of dimension D, may be decoded from the neuronal activities in a population as a linear combination of certain decoding functions φi(x), with coefficients given by the N firing rates ai(t) (generally with D ≪ N). We show how the neuronal encoding process may be described by projecting a set of complementary encoding functions [Formula: see text]i(x) on the probability density ρ(x; t), and passing the result through a rectifying nonlinear activation function. We show how both encoders [Formula: see text]i (x) and decoders φi(x) may be determined by minimizing cost functions that quantify the inaccuracy of the representation. Expressing a given computation in terms of manipulation and transformation of probabilities, we show how this representation leads to a neural circuit that can carry out the required computation within a consistent Bayesian framework, with the synaptic weights being explicitly generated in terms of encoders, decoders, conditional probabilities, and priors.
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Soldatova, Larisa N., Andrey Rzhetsky, Kurt De Grave, and Ross D. King. "Representation of probabilistic scientific knowledge." Journal of Biomedical Semantics 4, Suppl 1 (2013): S7. http://dx.doi.org/10.1186/2041-1480-4-s1-s7.

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Haba, Z. "Probabilistic representation of quantum dynamics." Physics Letters A 175, no. 6 (April 1993): 371–76. http://dx.doi.org/10.1016/0375-9601(93)90984-8.

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Dissertations / Theses on the topic "Probabilistic representation"

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Helmkay, Owen. "Information representation, problem format, and mental algorithms in probabilistic reasoning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ66153.pdf.

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Tarrago, Pierre. "Non-commutative generalization of some probabilistic results from representation theory." Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1123/document.

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Le sujet de cette thèse est la généralisation non-commutative de résultats probabilistes venant de la théorie des représentations. Les résultats obtenus se divisent en trois parties distinctes. Dans la première partie de la thèse, le concept de groupe quantique easy est étendu au cas unitaire. Tout d'abord, nous donnons une classification de l'ensemble des groupes quantiques easy unitaires dans le cas libre et classique. Nous étendons ensuite les résultats probabilistes de au cas unitaire. La deuxième partie de la thèse est consacrée à une étude du produit en couronne libre. Dans un premier temps, nous décrivons les entrelaceurs des représentations dans le cas particulier d'un produit en couronne libre avec le groupe symétrique libre: cette description permet également d'obtenir plusieurs résultats probabilistes. Dans un deuxième temps, nous établissons un lien entre le produit en couronne libre et les algèbres planaires: ce lien mène à une preuve d'une conjecture de Banica et Bichon. Dans la troisième partie de la thèse, nous étudions un analoque du graphe de Young qui encode la structure multiplicative des fonctions fondamentales quasi-symétriques. La frontière minimale de ce graphe a déjà été décrite par Gnedin et Olshanski. Nous prouvons que la frontière minimale coïncide avec la frontière de Martin. Au cours de cette preuve, nous montrons plusieurs résultats combinatoires asymptotiques concernant les diagrammes de Young en ruban
The subject of this thesis is the non-commutative generalization of some probabilistic results that occur in representation theory. The results of the thesis are divided into three different parts. In the first part of the thesis, we classify all unitary easy quantum groups whose intertwiner spaces are described by non-crossing partitions, and develop the Weingarten calculus on these quantum groups. As an application of the previous work, we recover the results of Diaconis and Shahshahani on the unitary group and extend those results to the free unitary group. In the second part of the thesis, we study the free wreath product. First, we study the free wreath product with the free symmetric group by giving a description of the intertwiner spaces: several probabilistic results are deduced from this description. Then, we relate the intertwiner spaces of a free wreath product with the free product of planar algebras, an object which has been defined by Bisch and Jones. This relation allows us to prove the conjecture of Banica and Bichon. In the last part of the thesis, we prove that the minimal and the Martin boundaries of a graph introduced by Gnedin and Olshanski are the same. In order to prove this, we give some precise estimates on the uniform standard filling of a large ribbon Young diagram. This yields several asymptotic results on the filling of large ribbon Young diagrams
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Shen, Amelia H. (Amelia Huimin). "Probabilistic representation and manipulation of Boolean functions using free Boolean diagrams." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/34087.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
Includes bibliographical references (p. 145-149).
by Amelia Huimin Shen.
Ph.D.
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Lloyd, James Robert. "Representation, learning, description and criticism of probabilistic models with applications to networks, functions and relational data." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709264.

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Vasudevan, Shrihari. "Spatial cognition for mobile robots : a hierarchical probabilistic concept-oriented representation of space." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17612.

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Lavis, Benjamin Mark Mechanical &amp Manufacturing Engineering Faculty of Engineering UNSW. "Spatially reconfigurable and non-parametric representation of dynamic bayesian beliefs." Publisher:University of New South Wales. Mechanical & Manufacturing Engineering, 2008. http://handle.unsw.edu.au/1959.4/41468.

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This thesis presents a means for representing and computing beliefs in the form of arbitrary probability density functions with a guarantee for the ongoing validity of such beliefs over indefinte time frames. The foremost aspect of this proposal is the introduction of a general, theoretical, solution to the guaranteed state estimation problem from within the recursive Bayesian estimation framework. The solution presented here determines the minimum space required, at each stage of the estimation process, to represent the belief with limited, or no, loss of information. Beyond this purely theoretical aspect, a number of numerical techniques, capable of determining the required space and performing the appropriate spatial reconfiguration, whilst also computing and representing the belief functions, are developed. This includes a new, hybrid particle-element approach to recursive Bayesian estimation. The advantage of spatial reconfiguration as presented here is that it ensures that the belief functions consider all plausible states of the target system, without altering the recursive Bayesian estimation equations used to form those beliefs. Furthermore, spatial reconfiguration as proposed in this dissertation enhances the estimation process since it allows computational resources to be concentrated on only those states considered plausible. Autonomous maritime search and rescue is used as a focus application throughout this dissertation since the searching-and-tracking requirements of the problem involve uncertainty, the use of arbitrary belief functions and dynamic target systems. Nevertheless, the theoretical development in this dissertation has been kept general and independent of an application, and as such the theory and techniques presented here may be applied to any problem involving dynamic Bayesian beliefs. A number of numerical experiments and simulations show the efficacy of the proposed spatially reconfigurable representations, not only in ensuring the validity of the belief functions over indefinite time frames, but also in reducing computation time and improving the accuracy of function approximation. Improvements of an order of magnitude were achieved when compared with traditional, spatially static representations.
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Geilke, Michael [Verfasser]. "Online density estimates : a probabilistic condensed representation of data for knowledge discovery / Michael Geilke." Mainz : Universitätsbibliothek Mainz, 2017. http://d-nb.info/1147611165/34.

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Zanitti, Gaston Ezequiel. "Development of a probabilistic domain-specific language for brain connectivity including heterogeneous knowledge representation." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG022.

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Grâce aux récents progrès technologiques, le chercheur en neurosciences dispose d'une quantité croissante de jeux de données pour étudier le cerveau. La multiplicité des travaux dédiés a également produit des ontologies encodant des connaissances à la pointe concernant les différentes aires, les schémas d'activation, les mots-clés associés aux études, etc. Il existe d'autre part une incertitude inhérente aux images cérébrales, du fait de la mise en correspondance entre voxels - ou pixels 3D - et points réels sur le cerveau de différents sujets. Malheureusement, à ce jour, aucun cadre unifié ne permet l'accès à cette mine de données hétérogènes avec l'incertitude associée, obligeant le chercheur à se tourner vers des outils ad hoc. Dans cette étude, nous présentons NeuroLang, un langage probabiliste basé sur de la logique de premier ordre, comprenant des règles existentielles, de l'incertitude probabiliste, l'intégration d'ontologies reposant sur l'hypothèse du monde ouvert, ainsi que des mécanismes garantissant une réponse aux requêtes résolvables, même sur de très grandes bases de données. Nous soutenons que NeuroLang, par l'expressivité de son langage de requête, contribuera à grandement améliorer la recherche en neurosciences, en donnant notamment la possibilité d'intégrer de manière transparente des données hétérogènes, telles que des ontologies avec des atlas cérébraux probabilistes. Dans ce cas-ci, des domaines cognitifs - à la granularité fine - et des régions cérébrales seront associés via un ensemble de critères formels, favorisant ainsi la communication et la reproductibilité des résultats d'études sur les fonctions cérébrales. Aussi croyons-nous que NeuroLang est à même de se positionner en tête sur ces approches numériques qui visent à formaliser la recherche neuroscientifique à grande échelle via la programmation probabiliste et logique du premier ordre
Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, keywords associated with studies, etc. Furthermore, there is inherent uncertainty associated with brain scans arising from the mapping between voxels -3D pixels- and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In this work we introduce NeuroLang, a probabilistic language based on first-order logic with existential rules, probabilistic uncertainty, ontologies integration under the open world assumption, and built-in mechanisms to guarantee tractable query answering over very large datasets. We propose that NeuroLang provides a substantial improvement to cognitive neuroscience research through the expressive power of its query language. We can leverage the ability of NeuroLang to seamlessly integrate useful heterogeneous data, such as ontologies and probabilistic brain atlases, to map fine-grained cognitive domains to brain regions through a set of formal criteria, promoting shareable and highly reproducible research on the domains of brain function. We believe that NeuroLang is well suited for leading computational approaches to formalize large-scale neuroscience research through probabilistic first-order logic programming
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Tarrago, Pierre [Verfasser], and Roland [Akademischer Betreuer] Speicher. "Non-commutative generalization of some probabilistic results from representation theory / Pierre Tarrago. Betreuer: Roland Speicher." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2015. http://d-nb.info/1079840249/34.

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Nayak, Sunita. "Representation and learning for sign language recognition." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002362.

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Books on the topic "Probabilistic representation"

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Aven, Terje. Uncertainty in risk assessment: The representation and treatment of uncertainties by probabilistic and non-probabilistic methods. Chichester, West Sussex, United Kingdom: Wiley, 2014.

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Fisseler, Jens. Learning and modeling with probabilistic conditional logic. Heidelberg: Ios Press, 2010.

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Felsberg, Michael. Probabilistic and Biologically Inspired Feature Representations. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-031-01822-0.

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Aven, Terje, Enrico Zio, Piero Baraldi, and Roger Flage. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Limited, John, 2014.

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Aven, Terje, Enrico Zio, Piero Baraldi, and Roger Flage. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Incorporated, John, 2013.

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Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Incorporated, John, 2013.

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Aven, Terje, Enrico Zio, Piero Baraldi, and Roger Flage. Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley & Sons, Incorporated, John, 2013.

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Baulieu, Laurent, John Iliopoulos, and Roland Sénéor. Functional Integrals and Probabilistic Amplitudes. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198788393.003.0008.

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Functional integrals and probabilistic amplitudes. Brief historical notes. The reconstruction of quantum mechanics from path integrals. The Feynman formulation. Definition and properties of the coherent states and the Bargmann representation.
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Classification and Probabilistic Representation of the Positive Solutions of a Semilinear Elliptic Equation. American Mathematical Society (AMS), 2004.

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Hancox, J., and J. Boardman. The Impact of an Alternative Representation of the Atmosphere on the Predictions of the Probabilistic Consequence Code CONDOR (Reports). AEA Technology Plc, 1992.

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Book chapters on the topic "Probabilistic representation"

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Cerf, Raphaël, and Joseba Dalmau. "Probabilistic Representation." In Probability Theory and Stochastic Modelling, 187–94. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08663-2_23.

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Goertzel, Ben, Matthew Iklé, Izabela Freire Goertzel, and Ari Heljakka. "Knowledge Representation." In Probabilistic Logic Networks, 1–17. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76872-4_2.

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Sucar, Luis Enrique. "Bayesian Networks: Representation and Inference." In Probabilistic Graphical Models, 101–36. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_7.

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Sucar, Luis Enrique. "Bayesian Networks: Representation and Inference." In Probabilistic Graphical Models, 111–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_7.

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Baudrit, Cédric, Didier Dubois, and Hélène Fargier. "Representation of Incomplete Probabilistic Information." In Soft Methodology and Random Information Systems, 149–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44465-7_17.

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Hommersom, Arjen. "Toward Probabilistic Analysis of Guidelines." In Knowledge Representation for Health-Care, 139–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18050-7_11.

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Belle, Vaishak. "Tractable Probabilistic Models for Ethical AI." In Graph-Based Representation and Reasoning, 3–8. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16663-1_1.

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Lambert, James H., and Priya Sarda. "Representation of Risk Scenarios via Euler Diagrams." In Probabilistic Safety Assessment and Management, 3148–52. London: Springer London, 2004. http://dx.doi.org/10.1007/978-0-85729-410-4_504.

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Le Gall, Jean-François. "The Probabilistic Representation of Positive Solutions." In Spatial Branching Processes, Random Snakes and Partial Differential Equations, 111–28. Basel: Birkhäuser Basel, 1999. http://dx.doi.org/10.1007/978-3-0348-8683-3_7.

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Beaudette, D. E., P. Roudier, and J. Skovlin. "Probabilistic Representation of Genetic Soil Horizons." In Progress in Soil Science, 281–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28295-4_18.

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Conference papers on the topic "Probabilistic representation"

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"Probabilistic Models for Semantic Representation." In The 1st International Workshop on Ontology for e-Technologies. SciTePress - Science and and Technology Publications, 2009. http://dx.doi.org/10.5220/0002222100130022.

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Lopes, Juan P. A., Fabiano S. Oliveira, and Paulo E. D. Pinto. "Probabilistic data structures applied to implicit graph representation." In XXXI Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/ctd.2018.3659.

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In recent years, probabilistic data structures have been extensively employed to handle large volumes of streaming data in a timely fashion. However, their use in algorithms on giant graphs has been poorly explored. We introduce the concept of probabilistic implicit graph representation, which can represent large graphs using much less memory asymptotically by allowing adjacency test to have a constant probability of false positives or false negatives. This is an extension from the concept of implicit graph representation, comprehensively studied by Muller and Spinrad. Based on that, we also introduce two novel representations using probabilistic data structures. The first uses Bloom filters to represent general graphs with the same space complexity as the adjacency matrix (outperforming it however for sparse graphs). The other uses MinHash to represent trees with lower space complexity than any deterministic implicit representation. Furthermore, we prove some theoretical limitations for the latter approach.
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Wu, Haoyi, and Kewei Tu. "Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation." In Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-acl.482.

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Da Silva, José L., Mohamed Erraoui, and Habib Ouerdiane. "Convolution Equation: Solution and Probabilistic Representation." In Proceedings of the 29th Conference. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814295437_0016.

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Ryabov, V., and A. Trudel. "Probabilistic temporal interval networks." In Proceedings. 11th International Symposium on Temporal Representation and Reasoning, 2004. TIME 2004. IEEE, 2004. http://dx.doi.org/10.1109/time.2004.1314421.

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Baier, Christel, Martin Diller, Clemens Dubslaff, Sarah Alice Gaggl, Holger Hermanns, and Nikolai Käfer. "Admissibility in Probabilistic Argumentation." In 18th International Conference on Principles of Knowledge Representation and Reasoning {KR-2021}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/kr.2021/9.

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Abstract argumentation is a prominent reasoning framework. It comes with a variety of semantics, and has lately been enhanced by probabilities to enable a quantitative treatment of argumentation. While admissibility is a fundamental notion in the classical setting, it has been merely reflected so far in the probabilistic setting. In this paper, we address the quantitative treatment of argumentation based on probabilistic notions of admissibility in a way that they form fully conservative extensions of classical notions. In particular, our building blocks are not the beliefs regarding single arguments. Instead we start from the fairly natural idea that whatever argumentation semantics is to be considered, semantics systematically induces constraints on the joint probability distribution on the sets of arguments. In some cases there might be many such distributions, even infinitely many ones, in other cases there may be one or none. Standard semantic notions are shown to induce such sets of constraints, and so do their probabilistic extensions. This allows them to be tackled by SMT solvers, as we demonstrate by a proof-of-concept implementation. We present a taxonomy of semantic notions, also in relation to published work, together with a running example illustrating our achievements.
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Chen, Mingda, and Kevin Gimpel. "Learning Probabilistic Sentence Representations from Paraphrases." In Proceedings of the 5th Workshop on Representation Learning for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.repl4nlp-1.3.

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Rocha, Victor Hugo Nascimento, and Fabio Gagliardi Cozman. "A Credal Least Undefined Stable Semantics for Probabilistic Logic Programs and Probabilistic Argumentation." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/31.

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We present an approach to probabilistic logic programming and probabilistic argumentation that combines elements of the L-stable semantics and the credal semantics. We derive the complexity of inferences, propose an extended version of argumentation graphs with a semantics that maps to the L- stable semantics, and introduce a definition for the probability of an argument.
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Liu, Jian wei, Hui dan Zhao, Run-kun Lu, and Xiong lin Luo. "Multi-view classifier based on Probabilistic Collaborative Representation and Latent Representation." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164584.

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Skryagin, Arseny, Wolfgang Stammer, Daniel Ochs, Devendra Singh Dhami, and Kristian Kersting. "Neural-Probabilistic Answer Set Programming." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/48.

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The goal of combining the robustness of neural networks and the expressivity of symbolic methods has rekindled the interest in Neuro-Symbolic AI. One specifically interesting branch of research is deep probabilistic programming languages (DPPLs) which carry out probabilistic logical programming via the probability estimations of deep neural networks. However, recent SOTA DPPL approaches allow only for limited conditional probabilistic queries and do not offer the power of true joint probability estimation. In our work, we propose an easy integration of tractable probabilistic inference within a DPPL. To this end we introduce SLASH, a novel DPPL that consists of Neural-Probabilistic Predicates (NPPs) and a logical program, united via answer set programming. NPPs are a novel design principle allowing for the unification of all deep model types and combinations thereof to be represented as a single probabilistic predicate. In this context, we introduce a novel +/- notation for answering various types of probabilistic queries by adjusting the atom notations of a predicate. We evaluate SLASH on the benchmark task of MNIST addition as well as novel tasks for DPPLs such as missing data prediction, generative learning and set prediction with state-of-the-art performance, thereby showing the effectiveness and generality of our method.
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Reports on the topic "Probabilistic representation"

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Sakhanenko, Nikita A., and George F. Luger. Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada491876.

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Zio, Enrico, and Nicola Pedroni. Literature review of methods for representing uncertainty. Fondation pour une culture de sécurité industrielle, December 2013. http://dx.doi.org/10.57071/124ure.

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This document provides a critical review of different frameworks for uncertainty analysis, in a risk analysis context: classical probabilistic analysis, imprecise probability (interval analysis), probability bound analysis, evidence theory, and possibility theory. The driver of the critical analysis is the decision-making process and the need to feed it with representative information derived from the risk assessment, to robustly support the decision. Technical details of the different frameworks are exposed only to the extent necessary to analyze and judge how these contribute to the communication of risk and the representation of the associated uncertainties to decision-makers, in the typical settings of high-consequence risk analysis of complex systems with limited knowledge on their behaviour.
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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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Zanoni, Wladimir, Jimena Romero, Nicolás Chuquimarca, and Emmanuel Abuelafia. Dealing with Hard-to-Reach Populations in Panel Data: Respondent-Driven Survey (RDS) and Attrition. Inter-American Development Bank, October 2023. http://dx.doi.org/10.18235/0005194.

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Hidden populations, such as irregular migrants, often elude traditional probabilistic sampling methods. In situations like these, chain-referral sampling techniques like Respondent-Driven Surveys (RDS) offer an effective solution. RDS, a variant of network sampling sometimes referred to as “snowball” sampling, estimates weights based on the network structures of friends and acquaintances formed during the sampling process. This ensures the samples are representative of the larger population. However, one significant limitation of these methods is the rigidity of the weights. When faced with participant attrition, recalibrating these weights to ensure continued representation poses a challenge. This technical note introduces a straightforward methodology to account for such attrition. Its applicability is demonstrated through a survey targeting Venezuelan migrants in Ecuador and Peru.
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Wilson, D., Daniel Breton, Lauren Waldrop, Danney Glaser, Ross Alter, Carl Hart, Wesley Barnes, et al. Signal propagation modeling in complex, three-dimensional environments. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40321.

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The Signal Physics Representation in Uncertain and Complex Environments (SPRUCE) work unit, part of the U.S. Army Engineer Research and Development Center (ERDC) Army Terrestrial-Environmental Modeling and Intelligence System (ARTEMIS) work package, focused on the creation of a suite of three-dimensional (3D) signal and sensor performance modeling capabilities that realistically capture propagation physics in urban, mountainous, forested, and other complex terrain environments. This report describes many of the developed technical capabilities. Particular highlights are (1) creation of a Java environmental data abstraction layer for 3D representation of the atmosphere and inhomogeneous terrain that ingests data from many common weather forecast models and terrain data formats, (2) extensions to the Environmental Awareness for Sensor and Emitter Employment (EASEE) software to enable 3D signal propagation modeling, (3) modeling of transmitter and receiver directivity functions in 3D including rotations of the transmitter and receiver platforms, (4) an Extensible Markup Language/JavaScript Object Notation (XML/JSON) interface to facilitate deployment of web services, (5) signal feature definitions and other support for infrasound modeling and for radio-frequency (RF) modeling in the very high frequency (VHF), ultra-high frequency (UHF), and super-high frequency (SHF) frequency ranges, and (6) probabilistic calculations for line-of-sight in complex terrain and vegetation.
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Mazzoni, Silvia, Nicholas Gregor, Linda Al Atik, Yousef Bozorgnia, David Welch, and Gregory Deierlein. Probabilistic Seismic Hazard Analysis and Selecting and Scaling of Ground-Motion Records (PEER-CEA Project). Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/zjdn7385.

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This report is one of a series of reports documenting the methods and findings of a multi-year, multi-disciplinary project coordinated by the Pacific Earthquake Engineering Research Center (PEER) and funded by the California Earthquake Authority (CEA). The overall project is titled “Quantifying the Performance of Retrofit of Cripple Walls and Sill Anchorage in Single-Family Wood-Frame Buildings,” henceforth referred to as the “PEER–CEA Project.” The overall objective of the PEER–CEA Project is to provide scientifically based information (e.g., testing, analysis, and resulting loss models) that measure and assess the effectiveness of seismic retrofit to reduce the risk of damage and associated losses (repair costs) of wood-frame houses with cripple wall and sill anchorage deficiencies as well as retrofitted conditions that address those deficiencies. Tasks that support and inform the loss-modeling effort are: (1) collecting and summarizing existing information and results of previous research on the performance of wood-frame houses; (2) identifying construction features to characterize alternative variants of wood-frame houses; (3) characterizing earthquake hazard and ground motions at representative sites in California; (4) developing cyclic loading protocols and conducting laboratory tests of cripple wall panels, wood-frame wall subassemblies, and sill anchorages to measure and document their response (strength and stiffness) under cyclic loading; and (5) the computer modeling, simulations, and the development of loss models as informed by a workshop with claims adjustors. This report is a product of Working Group 3 (WG3), Task 3.1: Selecting and Scaling Ground-motion records. The objective of Task 3.1 is to provide suites of ground motions to be used by other working groups (WGs), especially Working Group 5: Analytical Modeling (WG5) for Simulation Studies. The ground motions used in the numerical simulations are intended to represent seismic hazard at the building site. The seismic hazard is dependent on the location of the site relative to seismic sources, the characteristics of the seismic sources in the region and the local soil conditions at the site. To achieve a proper representation of hazard across the State of California, ten sites were selected, and a site-specific probabilistic seismic hazard analysis (PSHA) was performed at each of these sites for both a soft soil (Vs30 = 270 m/sec) and a stiff soil (Vs30=760 m/sec). The PSHA used the UCERF3 seismic source model, which represents the latest seismic source model adopted by the USGS [2013] and NGA-West2 ground-motion models. The PSHA was carried out for structural periods ranging from 0.01 to 10 sec. At each site and soil class, the results from the PSHA—hazard curves, hazard deaggregation, and uniform-hazard spectra (UHS)—were extracted for a series of ten return periods, prescribed by WG5 and WG6, ranging from 15.5–2500 years. For each case (site, soil class, and return period), the UHS was used as the target spectrum for selection and modification of a suite of ground motions. Additionally, another set of target spectra based on “Conditional Spectra” (CS), which are more realistic than UHS, was developed [Baker and Lee 2018]. The Conditional Spectra are defined by the median (Conditional Mean Spectrum) and a period-dependent variance. A suite of at least 40 record pairs (horizontal) were selected and modified for each return period and target-spectrum type. Thus, for each ground-motion suite, 40 or more record pairs were selected using the deaggregation of the hazard, resulting in more than 200 record pairs per target-spectrum type at each site. The suites contained more than 40 records in case some were rejected by the modelers due to secondary characteristics; however, none were rejected, and the complete set was used. For the case of UHS as the target spectrum, the selected motions were modified (scaled) such that the average of the median spectrum (RotD50) [Boore 2010] of the ground-motion pairs follow the target spectrum closely within the period range of interest to the analysts. In communications with WG5 researchers, for ground-motion (time histories, or time series) selection and modification, a period range between 0.01–2.0 sec was selected for this specific application for the project. The duration metrics and pulse characteristics of the records were also used in the final selection of ground motions. The damping ratio for the PSHA and ground-motion target spectra was set to 5%, which is standard practice in engineering applications. For the cases where the CS was used as the target spectrum, the ground-motion suites were selected and scaled using a modified version of the conditional spectrum ground-motion selection tool (CS-GMS tool) developed by Baker and Lee [2018]. This tool selects and scales a suite of ground motions to meet both the median and the user-defined variability. This variability is defined by the relationship developed by Baker and Jayaram [2008]. The computation of CS requires a structural period for the conditional model. In collaboration with WG5 researchers, a conditioning period of 0.25 sec was selected as a representative of the fundamental mode of vibration of the buildings of interest in this study. Working Group 5 carried out a sensitivity analysis of using other conditioning periods, and the results and discussion of selection of conditioning period are reported in Section 4 of the WG5 PEER report entitled Technical Background Report for Structural Analysis and Performance Assessment. The WG3.1 report presents a summary of the selected sites, the seismic-source characterization model, and the ground-motion characterization model used in the PSHA, followed by selection and modification of suites of ground motions. The Record Sequence Number (RSN) and the associated scale factors are tabulated in the Appendices of this report, and the actual time-series files can be downloaded from the PEER Ground-motion database Portal (https://ngawest2.berkeley.edu/)(link is external).
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Hadley, Isabel. PR164-205102-R01 Application of Probabilistic Fracture Mechanics to Engineering Critical Assessment. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2021. http://dx.doi.org/10.55274/r0012093.

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This report summarizes the results of a series of deterministic and probabilistic fracture and fatigue calculations carried out in order to: ? Demonstrate that ProCW correctly implements probabilistic ECA, eg by comparing selected deterministic and probabilistic calculations, ? Show the effect of the choice of K-solution on the fatigue life and POF of pipes containing a circumferential flaw, ? Implement a two-stage probabilistic model of fatigue crack growth, in both air and marine environments, ? Consider the effects of modelling the fatigue crack growth threshold probabilistically, ? Demonstrate the use of ProCW for a representative riser geometry and a complex loading spectrum, ? For the same riser geometry/loading scenario, compare the POF implied by the use of design fatigue safety factors given in DNVGL-ST-F101 [2], DNVGL-RP-F204 [3] and DNVGL-RP-F201 [4] with the POF calculated directly from probabilistic calculations. There is a related webinar.
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Pfeifer, Dietmar. Some General Probabilistic Estimations for the Rate of Convergence in Operator Semigroup Representations. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada161359.

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Sanderson, Dylan, and Mark Gravens. Representative Storm Selection Tool : an automated procedure for the selection of representative storm events from a probabilistic database. Coastal and Hydraulics Laboratory (U.S.), May 2018. http://dx.doi.org/10.21079/11681/26829.

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Gravens, Mark, and Dylan Sanderson. Identification and selection of representative storm events from a probabilistic storm data base. Coastal and Hydraulics Laboratory (U.S.), January 2018. http://dx.doi.org/10.21079/11681/26341.

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