Results 211 to 220 of about 166,561 (260)

Vagueness and Bayesian probability

IEEE Transactions on Fuzzy Systems, 1994
This 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, 2003
A 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, 2005
SummaryReproductive 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, 1986
A 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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INDUCTION, PROBABILITY, AND BAYESIAN EPISTEMOLOGY

2003
In 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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Bayesian Probability Theory

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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Determining the conditional probabilities in Bayesian networks

2003
Summary: Bayesian networks are used to illustrate how the probability of having a disease can be updated given the results from clinical tests. The problem of diagnosis, that is of determining whether a certain disease is present, \(D\), or absent, \(D'\), based on the result of a medical test, is discussed.
OLMUŞ, Hülya, ERBAŞ, S. Oral
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ON BAYESIAN LOGICAL PROBABILITY

ETS Research Bulletin Series, 1964
ABSTRACTIn 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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Bayesian Probability Approach to ADHD Appraisal

2009
Accurate diagnosis of attentional disorders such as attention-deficit hyperactivity disorder (ADHD) is imperative because there are multiple negative psychosocial sequelae related to undiagnosed and untreated ADHD. Early and accurate detection can lead to effective intervention and prevention of negative sequelae.
Raina, Robeva, Jennifer Kim, Penberthy
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Probabilities: Bayesian Classifiers

2015
The 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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