bayesian reasoning research papers 101

bayesian reasoning research papers 101


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Bayesian learning on graphs for reasoning on image time-series
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Satellite image time-series (SITS) are multidimensional signals of high complexity. Their main characteristics are spatio-temporal patterns which describes the scene dynamics. The information contained in SITS was coded using Bayesian methods, resulting in a graph

Bayesian Networks Representation and Reasoning
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Bayesian network are a knowledge representation formalism for reasoning under uncertainty. A Bayesian network is a direct acyclic graph encoding assumptions of conditional independence. In a Bayesian network, nodes are stochastic variables and

Bayesian modeling and reasoning for real world robotics: Basics and examples
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Cognition and Reasoning with uncertain and partial knowledge is a challenge for autonomous mobile robotics. Previous robotics systems based on a purely logical or geometrical paradigm are limited in their ability to deal with partial or uncertain knowledge

Bayesian networks for logical reasoning
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Despite the fact that deductive logic is concerned with propositions that are certainly true or certainly false, logi- cal reasoning takes place in a context of very little certainty. In fact the very search for a proof of a proposition is usually a search for certainty: we are unsure about the

Agents and causes: A Bayesian error attribution model of causal reasoning
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ABSTRACT One of the most fundamental assumptions underlying causal Bayes nets is the Markov constraint. According to this constraint, an inference between a cause and an effect should be invariant across conditions in which other effects of this cause are present or

Causal Discovery and Reasoning for Intrusion Detection using Bayesian Network,
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ABSTRACT Computer security is essential in information technology world today; confidentiality, availability and integrity of data are the aspects concerned. Firewall has been widely deployed as a protection but it is no longer adequate to against the intelligent

Fuzzy reasoning and optimization based on a generalized Bayesian network
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ABSTRACT Bayesian networks have been widely used as the knowledge bases with uncertainty. However, in most literatures, the uncertainty measure in Bayesian networks are limited in probability distributions and crisp variables, which restricts the practical

Graphical reasoning with Bayesian networks
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Nowadays, Bayesian networks are seen by many researchers as standard tools for reasoning with uncertainty. Despite the fact that Bayesian networks are graphical representations, representing dependence and independence information, normally the

BAYESIAN NETWCRKS: A MODEL CF SELF-ACTIVATED MEMORY FOR EVIDENTIALREASONING
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Abstract The paper reports recent results from the theory of Bayesian networks, which offer a viable formalism for realizing the computational objectives of connectionist models of knowledge. In particular, we show that the Bayesian network formalism is supportive of

Approximate explanation of reasoning in Bayesian networks
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ABSTRACT We address the question of explaining the computation of the posterior probability of a node in a Bayesian network. In polytrees, this probability can be explained by decomposing it into a product of causal terms (from the ancestors) and diagnostic terms (

Reasoning about remote data in cdps with distributed bayesian neworks
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ABSTRACT. Existing Cooperative Distributed Problem Solving systems frequently employ fixed coordination strategies to achieve global consistency or global optimality. However, these strategies generally do not exploit the characteristics of the particular problem they are

Reasoning patterns in bayesian games
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ABSTRACT Game theory has been a useful in analyzing the structure and behavior of multi- agent systems. However, real world systems exhibit complexity and uncertainty which often impede the direct application of game-theoretic analyses. This often happens because

On the computability and complexity of Bayesian reasoning
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ABSTRACT If we consider the claim made by some cognitive scientists that the mind performs Bayesian reasoning, and if we simultaneously accept the Physical Church-Turing thesis and thus believe that the computational power of the mind is no more than that of a Turing

theory of probability is at bottom nothing more than good sense reduced to a calculus.-Pierre Laplace (1814)Abstract. Bayesian reasoning has been applied
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Pierre Laplace (1814)Abstract. Bayesian reasoning has been applied formally to statistical inference, machine learning and analyzing scientific method. Here I apply it informally to more common forms of inference, namely natural language arguments. I

A Neural Implementation of Conceptual Hierarchies with Bayesian Reasoning
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POPFP Myllymaki, H Tirri ,reference.kfupm.edu.sa ABSTRACT We present a scheme for translating high-level descriptions of conceptual hierarchies into a neural network representation. The intuitive semantics of a conceptual hierarchy is provided by a Bayesian net, and the neural network implementation provably

Application of Bayesian Reasoning and the Maximum Entropy Method to Some Reconstruction Problems
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KW Fornalskia, G Parzycha, M Pylaka, D Satulab ,przyrbwn.icm.edu.pl Experimental data always bear uncertainties. The researcher needs usually either to fit certain function to the measured points or to reconstruct certain distribution which is hidden in the experiment. This happens eg when one measures a spectrum which usually has

Bayesian reasoning for sensor group-queries and diagnosis
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As large-scale sensor networks are being deployed with the objective of collecting quality data to support user queries and decision-making, the role of a scalable query model becomes increasingly critical. An effective query model should scale well with large

The Potential Distortion of Bayesian Reasoning
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FJ Yang ,ww1.ucmss.com ABSTRACT The Bayesian theorem was formulated in the 18th century and has been adopted as the theoretical basis of uncertainty management as well as the stochastic foundation for forecast-oriented expert systems. Through Bayesian reasoning, people accumulate

Bayesian Network and Variable Elimination Algorithm for Reasoning under Uncertainty
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SRKP Jaladi, D Devarapalli ABSTRACT A common task for a Bayesian network is to perform inference by computing to determine various probabilities of interest from the model. We are using an algorithm for construction of Bayesian network from given data input from several data sources such as

Pp. 237-248 in Probabilistic Reasoning and Bayesian Belief Networks, edited by A. Gammerman. Alfred Waller Publishers, Suffolk 1995.
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P Myllymvaki, H Tirri ,kddresearch.org ABSTRACT Given a set of samples of an unknown probability distribution, we study the problem of constructing a good approximative Bayesian network model of the probability distribution in question. This task can be viewed as a search problem, where the goal is to find a

The Effects of Visual Representation on Bayesian Decision Making and Bayesian Reasoning
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This study examines whether visual aids may improve Bayesian reasoning and Bayesian decision making. Three visual aids were examined relative to a text-only condition: a frequency tree, a frequency grid, and a faces grid. Participants were given a hypothetical

Using bayesian reasoning from sensor network for indoor surveillance
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ABSTRACT. In this paper we define a Bayesian framework that uses noisy, but redundant data from a network of sensors that include multiple sensor streams of different types. It merges the data with the contextual and domain knowledge that is provided by both the physical

Study on Spatial Knowledge Representation and Reasoning Based on Bayesian Networks
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ABSTRACT Spatial information plays an essential role on the progress of science and technology, and has a profound impact on economic growth and society progress in the twenty-first century. Spatial knowledge representation and reasoning are very important

Explicit Bayesian Reasoning with Frequencies, Probabilities, and Surprisals
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H Prime, TR Shultz ,csjarchive.cogsci.rpi.edu ABSTRACT To explore human deviations from Bayes rule in numerically explicit problems, prior and likelihood probabilities or frequencies are manipulated and their effects on posterior probabilities or surprisals are measured. Results show that people use both priors and

A non-Bayesian Account of the Causal Reasoning in Sobel, TenenbaumGopnik (2004)
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IBG Bara, L Barsalou, M Bucciarelli ,serban.musca.perso.neuf.fr ABSTRACT Sobel, TenenbaumGopnik (2004) investigated the development of causal inferences in preschoolers in three experiments with tasks adapted from conditioning literature (backwards blocking and screening-off) and concluded from this indirect

A hybrid metareasoning architecture combining case-based reasoning and Bayesiannetworks
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Abstract. In complex domains, a single type of knowledge and reasoning method is often not sufficient for a decision support system to address the variety of tasks a user performs. It is often necessary to determine which reasoning method would be the most appropriate for

A non-Bayesian Account of the Causal Reasoning in Sobel, TenenbaumGopnik (2004)
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SC Musca, G Vallabha ,csjarchive.cogsci.rpi.edu ABSTRACT Sobel, TenenbaumGopnik (2004) investigated the development of causal inferences in preschoolers in three experiments with tasks adapted from conditioning literature (backwards blocking and screening-off) and concluded from this indirect

Probabilistic Reasoning with Naïve Bayes and Bayesian Networks
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Objectives The aim of this project is to expose students to two important reasoning and learning algorithms–Naïve Bayes and Bayesian Networks, and to explore their relationship in the context of solving practical classification problems. In particular, the objectives of the

Bayesian probabilistic reasoning applied to mathematical epidemiology for predictive spatiotemporal analysis of infectious diseases
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Demographic based risk analysis lends to observation of varied spectrum of HIV risk among the different demographic subgroups. A methodology using hidden Markov models is introduced that enables to investigate the impact of social behavioral interactions in the

Automated Bayesian network Integration based on Ontology for Reasoning Object Existence of Service Robot
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YS Song, SB Cho ,nt.ntnu.no ABSTRACT Object detection of service robots is very important for their service. Most of services such as delivery, errand of users are related to objects. Conventional methods are based on the geometric models in static industrial environments, but they have limitations

Distributed reasoning and learning in Bayesian expert systems
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FJDJ Mira ,mathcs.emory.edu ABSTRACT This paper presents Bayesian networks as a framework for distributed reasoning in expert systems. We discuss methods for evidence propagation, for learning, with emphasis on sequential learning, and for generating linguistic explanations. When a parallel

From Qualitative Reasoning Models to Bayesian-based Learner Modeling
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Abstract Assessing the knowledge of a student is a fundamental part of intelligent learning environments. We present a Bayesian network based approach to dealing with uncertainty when estimating a learner s state of knowledge in the context of Qualitative Reasoning (

BAYESIAN NETWCRKS: A MODEL CF SELF-ACTIV MEMORY FOR EVIDENTIAL REASONING
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Abstract The paper reports recent results from the theory of Bayesian networks, which oiier a viable formalism for realizing the computational objectives of connectionist models of knowledge. In particular, We show that the Bayesian network formalism is supportive of

Monte Carlo-Based Bayesian Group Object Tracking and Causal Reasoning
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We present algorithms for tracking and reasoning of local traits in the subsystem level based on the observed emergent behavior of multiple coordinated groups in potentially cluttered environments. Our proposed Bayesian inference schemes, which are primarily based on (

Using EvidencesInterests Reasoning to Reduce the Structure of Bayesian Networks
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S KABAK ,samuel.kabak.free.fr ABSTRACT A method is proposed to create from an initial Bayesian Network, knowing the observed and the queried variables, one or more induced Bayesian Networks that give the same marginals for the queried variables. The induced networks are smaller than the

A Bayesian Model of Pedagogical Reasoning
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Abstract Much of learning and reasoning occurs in pedagogical situations–situations in which teachers choose examples with the goal of having a learner infer the concept the teacher has in mind. In this paper, we present a model of teaching and learning in

INTEGRATING REASONING ABOUT ACTIONS AND BAYESIAN NETWORKS
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autonomous agents interacting with an incompletely known world need to reason logically about the effects of their actions and sensor information they acquire over time. In realistic

Integrating BDI and Bayesian Networks in Agent Reasoning
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Abstract. This paper focuses on a Social Agent which has been modeled using probabilistic networks and acts in an educational application. Using the Social Agent as a testbed, we present a way to perform the deliberation process in BDI (Belief-Desire-Intention) and

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