Bayesian theory and applications / edited by Paul Damien ... [et. al.].

Format:

Book

Contributor:

Damien, Paul.

Language:

English

Subjects (All):

Bayesian statistical decision theory.

Mathematics.

Physical Description:

1 online resource (717 p.)

Place of Publication:

Oxford : Oxford University Press, 2013.

Language Note:

English

Summary:

This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field.

Contents:

Cover; Contents; Contributors; Introduction; Part I: Exchangeability; 1 Observables and models: exchangeability and the inductive argument; 2 Exchangeability and its ramifications; Part II: Hierarchical Models; 3 Hierarchical modelling; 4 Bayesian hierarchical kernel machines for nonlinear regression and classification; 5 Flexible Bayesian modelling for clustered categorical responses in developmental toxicology; Part III: Markov Chain Monte Carlo; 6 Markov chain Monte Carlo methods; 7 Advances in Markov chain Monte Carlo; Part IV: Dynamic Models; 8 Bayesian dynamic modelling

9 Hierarchical modelling in time series: the factor analytic approach10 Dynamic and spatial modelling of block maxima extremes; Part V: Sequential Monte Carlo; 11 Online Bayesian learning in dynamic models: an illustrative introduction to particle methods; 12 Semi-supervised classification of texts using particle learning for probabilistic automata; Part VI: Nonparametrics; 13 Bayesian nonparametrics; 14 Geometric weight priors and their applications; 15 Revisiting Bayesian curve fitting using multivariate normal mixtures; Part VII: Spline Models and Copulas

16 Applications of Bayesian smoothing splines17 Bayesian approaches to copula modelling; Part VIII: Model Elaboration and Prior Distributions; 18 Hypothesis testing and model uncertainty; 19 Proper and non-informative conjugate priors for exponential family models; 20 Bayesian model specification: heuristics and examples; 21 Case studies in Bayesian screening for time-varying model structure: the partition problem; Part IX: Regressions and Model Averaging; 22 Bayesian regression structure discovery; 23 Gibbs sampling for ordinary, robust and logistic regression with Laplace priors

24 Bayesian model averaging in the M-open frameworkPart X: Finance and Actuarial Science; 25 Asset allocation in finance: a Bayesian perspective; 26 Markov chain Monte Carlo methods in corporate finance; 27 Actuarial credibility theory and Bayesian statistics-the story of a special evolution; Part XI: Medicine and Biostatistics; 28 Bayesian models in biostatistics and medicine; 29 Subgroup analysis; 30 Surviving fully Bayesian nonparametric regression models; Part XII: Inverse Problems and Applications; 31 Inverse problems

32 Approximate marginalization over modelling errors and uncertainties in inverse problems33 Bayesian reconstruction of particle beam phase space; Adrian Smith's research supervision (PhD); Adrian Smith's publications; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U; V; W; Z

Notes:

Description based upon print version of record.

Description based on print version record.

Includes bibliographical references.

ISBN:

0-19-873907-9

1-283-95040-5

0-19-164700-4

OCLC:

922972076