Chapter: Observables and models: exchangeability and the inductive argument

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Home > Bayesian Theory and Applications > Observables and models: exchangeability and the inductive argument

Bayesian Theory and Applications |

Paul Damien; Petros Dellaportas; Nicholas G. Polson; David A. Stephens

Oxford University Press

Observables and models: exchangeability and the inductive argument

Chapter Info

Authors

Micheal Goldstein

ARI Id

1667711143084_16288494

Access

Not Available Free

Chapter URL

https://academic.oup.com/book/12043/chapter/161347461

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Chapters/Headings	Author(s)	Pages	Info
Front Matter
Copyright Page
Dedication
Contributors
Introduction
Observables and models: exchangeability and the inductive argument	Micheal Goldstein
Exchangeability and its ramifications	A. Philip Dawid
Hierarchical modelling	Alan E Gelfand; Souparno Ghosh
4 Bayesian hierarchical kernel machines for nonlinear regression and classification	Sounak Chakraborty
Flexible Bayesian modelling for clustered categorical responses in developmental toxicology	Kottas Athanasios; Fronczyk Kassandra
Markov chain Monte Carlo methods	Chib Siddhartha
Advances in Markov chain Monte Carlo	Griffin Jim E; Stephens David A
Bayesian dynamic modelling	West Mike
Hierarchical modelling in time series: the factor analytic approach	Gamerman Dani; Salazar Esther
Dynamic and spatial modelling of block maxima extremes	Gabriel Huerta; Glenn A. Stark
11 Online Bayesian learning in dynamic models: an illustrative introduction to particle methods	Hedibert F Lopes; Carlos M Carvalho
Semi-supervised classification of texts using particle learning for probabilistic automata	ANA PAULA SALES
13 Bayesian nonparametrics	Stephen G Walker
Geometric weight priors and their applications	Ramsés H. Mena
Revisiting Bayesian curve fitting using multivariate normal mixtures	Stephen G Walker; George Karabatsos
Chapters/Headings	Author(s)	Pages	Info

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