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Bayesian Inference in Statistical Analysis . Author: George E. P. Box and George C. Tiao Publication data: Addison Wesley Publishing Co., 1973. Reprinted 1992: Wiley ISBN 0471574287 Description: The first complete analysis of Bayesian Inference for many statistical problems.
Exploratory analysis of Bayesian models is an adaptation or extension of the exploratory data analysis approach to the needs and peculiarities of Bayesian modeling. In the words of Persi Diaconis: [16] Exploratory data analysis seeks to reveal structure, or simple descriptions in data. We look at numbers or graphs and try to find patterns.
Kruschke's popular textbook, Doing Bayesian Data Analysis, [2] was notable for its accessibility and unique scaffolding of concepts. The first half of the book used the simplest type of data (i.e., dichotomous values) for presenting all the fundamental concepts of Bayesian analysis, including generalized Bayesian power analysis and sample-size planning.
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often ...
As statistics and data sets have become more complex, [a] [b] questions have arisen regarding the validity of models and the inferences drawn from them. There is a wide range of conflicting opinions on modelling. Models can be based on scientific theory or ad hoc data analysis, each employing different methods. Advocates exist for each approach ...
Andrew Eric Gelman (born February 11, 1965) is an American statistician and professor of statistics and political science at Columbia University. Gelman received bachelor of science degrees in mathematics and in physics from MIT , where he was a National Merit Scholar , in 1986.
Here is a Bayesian analysis of a female patient with a family history of cystic fibrosis (CF) who has tested negative for CF, demonstrating how the method was used to determine her risk of having a child born with CF: because the patient is unaffected, she is either homozygous for the wild-type allele, or heterozygous.
Bayesian inference (/ ˈ b eɪ z i ə n / BAY-zee-ən or / ˈ b eɪ ʒ ən / BAY-zhən) [1] is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available.