Results 241 to 250 of about 471,788 (278)
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IEEE Transactions on Systems, Man, and Cybernetics, 1971
Summary: It is shown that a certain weighted average of the prier distribution and the empirical distribution yields an estimate of the posterior distribution that is consistent with Bayes theorem. A comparison of this approach and conventional parametric Bayesian estimation is made for some specific cases.
David R. Cunningham, Arthur M. Breipohl
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Summary: It is shown that a certain weighted average of the prier distribution and the empirical distribution yields an estimate of the posterior distribution that is consistent with Bayes theorem. A comparison of this approach and conventional parametric Bayesian estimation is made for some specific cases.
David R. Cunningham, Arthur M. Breipohl
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Bayesianism without learning [PDF]
Research in Economics, 1999 Abstract According to the standard definition, a Bayesian agent is one who forms his posterior belief by conditioning his prior belief on what he has learned, that is, on facts of which he has become certain. Here it is shown that Bayesianism can be described without assuming that the agent acquires any certain information; an agent is Bayesian if ...
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Bayesian learning of Bayesian networks with informative priors
Annals of Mathematics and Artificial Intelligence, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nicos Angelopoulos, James Cussens
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Trends in Cognitive Sciences, 2006
The Bayesian approach to belief updating is par excellence a theory of learning--a theory of how beliefs should be revised in the light of new evidence. Yet despite its considerable utility as a framework for understanding cognition, it has not been prominent in theorizing about elementary learning processes in animals and humans.
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The Bayesian approach to belief updating is par excellence a theory of learning--a theory of how beliefs should be revised in the light of new evidence. Yet despite its considerable utility as a framework for understanding cognition, it has not been prominent in theorizing about elementary learning processes in animals and humans.
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Bayesian Adversarial Learning. [PDF]
, 2018 Deep neural networks have been known to be vulnerable to adversarial attacks, raising lots of security concerns in the practical deployment. Popular defensive approaches can be formulated as a (distributionally) robust optimization problem, which minimizes a "point estimate" of worst-case loss derived from either per-datum perturbation or adversary ...
Ye, Nanyang, Zhu, Zhanxing
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A Bayesian Approach for Learning Bayesian Network Structures
Lobachevskii Journal of MathematicsWe introduce a Bayesian approach method based on the Gibbs sampler for learning the Bayesian Network structure. For this, the existence and the direction of the edges are specified by a set of parameters. We use the non-informative discrete uniform prior to these parameters.
Zareifard, Hamid +3 more
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A Theory of Bayesian Learning Systems
IEEE Transactions on Systems Science and Cybernetics, 1969Efforts are made to simplify the implementation and to improve the flexibility of Bayesian learning systems. Using a truncated series expansion to represent a pattern class, a simplified structure is shown with nearly optimal performance. A criterion of determining the learning sample size is given so that after taking a sufficient number of learning ...
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Bayesian NL Interpretation and Learning
2011Everyday natural language communication is normally successful, even though contemporary computational linguistics has shown that NL is characterised by very high degree of ambiguity and the results of stochastic methods are not good enough to explain the high success rate.
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2002
Learning from the Bayesian perspective can be described simply as the modification of opinion based on experience. This is in contrast to the Classical or “frequentist” approach that begins with no prior opinion, and inferences are based strictly on information obtained from a random sample selected from the population.
Paula Macrossan, Kerrie Mengersen
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Learning from the Bayesian perspective can be described simply as the modification of opinion based on experience. This is in contrast to the Classical or “frequentist” approach that begins with no prior opinion, and inferences are based strictly on information obtained from a random sample selected from the population.
Paula Macrossan, Kerrie Mengersen
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Abstract In Chapter 9 we derive Bayesian inference as an adaptive behaviour that emerges through natural selection in certain stochastic environments. Such behaviour arises purely through the forces of evolution, despite the fact that the population consists of mindless individuals without any ability to reason, act strategically, or ...
Andrew W. Lo, Ruixun Zhang
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Andrew W. Lo, Ruixun Zhang
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