Results 211 to 220 of about 18,600 (242)

Reasoning patterns in Bayesian games

International Joint Conference on Autonomous Agents and Multiagent Systems, 2011
Bayesian games have been traditionally employed to describe and analyze situations in which players have private information or are uncertain about the game being played. However, computing Bayes-Nash equilibria can be costly, and becomes even more so if the common prior assumption (CPA) has to be abandoned, which is sometimes necessary for a faithful ...
Dimitrios Antos, Avi Pfeffer
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BAYESIAN NETWORK REASONING WITH UNCERTAIN EVIDENCES

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2010
This paper investigates the problem of belief update in Bayesian networks (BN) with uncertain evidence. Two types of uncertain evidences are identified: virtual evidence (reflecting the uncertainty one has about a reported observation) and soft evidence (reflecting the uncertainty of an event one observes).
Yun Peng 0001, Shenyong Zhang, Rong Pan
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First-order Bayesian reasoning

1998
This paper briefly discusses problems with traditional Bayesian networks, and previous attempts at overcoming those problems, as a motivation for formulating a first-order knowledge based approach to Bayesian inference. The proposed first-order knowledge based approach endeavours to address each of the traditional Bayesian network problems.
Ingrid Fabian, Dale A. Lambert
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Modeling and Reasoning with Bayesian Networks

2009
This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity ...
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Applying Bayesian reasoning to electrocardiogram interpretation

Journal of Electrocardiology, 2023
Electrocardiograms (ECGs) are a cornerstone in cardiac care. Traditional statistical metrics like sensitivity and specificity are commonly used for diagnostic evaluations but are limited when applied in clinical settings due to their inability to incorporate pre-test likelihoods or individual patient context.
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The Scope of Bayesian Reasoning

PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, 1992
The Bayesian view of inference has become popular in philosophy in recent years. Scientific Reasoning: a Bayesian Approach, by Colin Howson and Peter Urbach, represents an articulate and persuasive defense of the Bayesian view. We focus on the theme of that book, and argue that there are difficulties with Bayesianism, and alternatives worth considering.
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A bayesian framework for case-based reasoning

1996
In this paper we present a probabilistic framework for case-based reasoning in data-intensive domains, where only weak prior knowledge is available. In such a probabilistic viewpoint the attributes are interpreted as random variables, and the case base is used to approximate the underlying joint probability distribution of the attributes.
Henry Tirri, Petri Kontkanen, Petri Myllymäki   +2 more
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On having no reason: dogmatism and Bayesian confirmation

Synthese, 2009
Recently in epistemology a number of authors have mounted Bayesian objections to dogmatism. These objections depend on a Bayesian principle of evidential confirmation: Evidence E confirms hypothesis H just in case Pr(H|E) > Pr(H). I argue using Keynes’ and Knight’s distinction between risk and uncertainty that the Bayesian principle fails to ...
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Eliciting an overlooked aspect of Bayesian reasoning

ACM SIGCSE Bulletin, 2007
Bayesian theorem is the theoretical basis of uncertainty management as well as the stochastic foundation for forecast-oriented expert systems. Mathematically, the reasoning steps can be represented by a sequence of probabilistic computations.
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Integrating Deep Learning and Bayesian Reasoning

2019
Deep learning (DL) is an excellent function estimator which has amazing result on perception tasks such as visualization recognition and text recognition. But, its inner architecture acts as a black box, because the users cannot understand why such decisions are made.
Sin Yin Tan, Wooi Ping Cheah, Shing Chiang Tan   +2 more
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