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On Fuzzy Probabilities in Bayesian Games
2011 Workshop-School on Theoretical Computer Science, 2011This work proposes an application of fuzzy probabilities, respecting an arithmetic restriction, in order to estimate unknown types of players in a bayesian game, thus configuring a fuzzy bayesian game. To this end, besides defining the operations needed for this application, a hypothetical example of a bayesian game is presented and then this ...
Tiago da Cruz Asmus +1 more
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Synthese, 2009
Bayesian decision theory is here construed as explicating a particular concept of rational choice and Bayesian probability is taken to be the concept of probability used in that theory. Bayesian probability is usually identified with the agent’s degrees of belief but that interpretation makes Bayesian decision theory a poor explication of the relevant ...
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Bayesian decision theory is here construed as explicating a particular concept of rational choice and Bayesian probability is taken to be the concept of probability used in that theory. Bayesian probability is usually identified with the agent’s degrees of belief but that interpretation makes Bayesian decision theory a poor explication of the relevant ...
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Vagueness and Bayesian probability
IEEE Transactions on Fuzzy Systems, 1994This paper is a response to Michael Laviolette and John W. Seaman Jr.'s ( ibid. vol.2, no.1, p.4 (1994)) position paper "The efficacy of fuzzy representations of uncertainty," which criticizes fuzzy representations of uncertainty, and suggests that Bayesian probability can do better.
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A RECURSION FORMULA FOR BAYESIAN PROBABILITIES
Psychological Reports, 2003A recursion formula for Bayes' formula is derived. The formula is useful in applications in which diagnoses are added in a stepwise way to predict a criterion. On each step, changes in various diagnostic measures can be easily evaluated.
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Bayesian Inferences on Predictors of Conception Probabilities
Biometrics, 2005SummaryReproductive scientists and couples attempting pregnancy are interested in identifying predictors of the day‐specific probabilities of conception in relation to the timing of a single intercourse act. Because most menstrual cycles have multiple days of intercourse, the occurrence of conception represents the aggregation across Bernoulli trials ...
Dunson, David B., Stanford, Joseph B.
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A Bayesian probability network
AIP Conference Proceedings, 1986A model of an associative neural network is developed in which the state of each node is described by a probability density. The realization of the network is based on the pairwise joint probabilities obtained from a training set of states. A positive definite ‘‘energy’’ functional of the probabilities may be constructed from Bayes’ rule of statistical
C. H. Anderson, E. Abrahams
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2014
From the basics to the forefront of modern research, this book presents all aspects of probability theory, statistics and data analysis from a Bayesian perspective for physicists and engineers. The book presents the roots, applications and numerical implementation of probability theory, and covers advanced topics such as maximum entropy distributions ...
Linden, W., Dose, V., Toussaint, U.
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From the basics to the forefront of modern research, this book presents all aspects of probability theory, statistics and data analysis from a Bayesian perspective for physicists and engineers. The book presents the roots, applications and numerical implementation of probability theory, and covers advanced topics such as maximum entropy distributions ...
Linden, W., Dose, V., Toussaint, U.
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INDUCTION, PROBABILITY, AND BAYESIAN EPISTEMOLOGY
2003In the last sixty years Finnish analytical philosophers have been extensively investigating induction and probability, and their role in empirical sciences. In this paper the main lines and outcomes of such studies are examined. In particular, the following issues are considered: von Wright’s theory of inductive elimination and his analysis of ...
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ON BAYESIAN LOGICAL PROBABILITY
ETS Research Bulletin Series, 1964ABSTRACTIn a recent paper (Edwards, Lindman, and Savage, 1963), psychologists have been urged to adopt a Bayesian personal probability approach to statistical inference. The purpose of this paper is to suggest that a Bayesian logical probability approach may be superior to the personal probability approach.
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Probabilities: Bayesian Classifiers
2015The earliest attempts to predict an example’s class based on the known attribute values go back to well before World War II—prehistory, by the standards of computer science. Of course, nobody used the term “machine learning,” in those days, but the goal was essentially the same as the one addressed in this book.
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