Bayesian Models for Categorical Data by Peter D. Congdon PDF

By Peter D. Congdon

ISBN-10: 0470092378

ISBN-13: 9780470092378

Using Bayesian equipment for the research of information has grown considerably in components as diversified as utilized statistics, psychology, economics and scientific technology. Bayesian tools for express info units out to demystify glossy Bayesian tools, making them available to scholars and researchers alike. Emphasizing using statistical computing and utilized information research, this e-book presents a accomplished advent to Bayesian equipment of specific outcomes.

• reports fresh Bayesian technique for express results (binary, count number and multinomial data).
• Considers lacking facts versions thoughts and non-standard versions (ZIP and adverse binomial).
• Evaluates time sequence and spatio-temporal versions for discrete data.
• beneficial properties dialogue of univariate and multivariate techniques.
• offers a collection of downloadable labored examples with documented WinBUGS code, on hand from an ftp site.

The author’s earlier 2 bestselling titles supplied a entire advent to the idea and alertness of Bayesian versions. Bayesian types for specific info maintains to construct upon this beginning through constructing their software to express, or discrete facts – essentially the most universal varieties of information on hand. The author’s transparent and logical technique makes the ebook obtainable to a variety of scholars and practitioners, together with these facing express facts in medication, sociology, psychology and epidemiology.

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DJ Þ, with parameter sets  ¼ ð1 ; 2 ; . . ; J ), prior model probabilities P(m ¼ j), and priors on parameters P(j jm ¼ j). Likelihood and prior specification is independent between models. The log-likelihood under model j is log[P(Yjj ފ ¼ Lðj jY), with deviance obtained as Dðj jYÞ ¼ À2Lðj jYÞ or as Dðj jYÞ ¼ À2½Lðj j YÞ À LðYjYފ. Consider the output from sampling stream ðtÞ ¼ ðtÞ ðtÞ ðtÞ ð1 ; . . ; j ; . . ; j Þ of length T. This may be obtained from parallel or sequential sampling.

One may wish to assess the gains from adopting heavy-tailed densities instead of the default normal errors assumption of multiple linear regression (West, 1984), or whether to adopt error forms adapted to the possibility of a small number of outlier points (Vernardinelli and Wasserman, 1991). For models j ¼ 1; . . ; J, let m be a multinomial model indicator, and j be the parameters under each model. 3) is based on prior model probabilities P(m ¼ j) and posterior model probabilities P(m ¼ jjY) after observing data.

2003). Then from the properties of the chi-square density, deà ¼ 0:5 VarðDðtÞ Þ. Both effective parameter estimates in practice include aspects of a model such as the precision of its parameters and predictions. 8 MULTIMODEL PERSPECTIVES VIA PARALLEL SAMPLING As in Congdon (2005a), consider data Y ¼ ðy1 ; . . ; yn Þ and a set of potential models j ¼ 1; . . ; J of dimensions d ¼ ðd1 ; d2 ; . . ; dJ Þ, with parameter sets  ¼ ð1 ; 2 ; . . ; J ), prior model probabilities P(m ¼ j), and priors on parameters P(j jm ¼ j).

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Bayesian Models for Categorical Data by Peter D. Congdon

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