Bayesian theory and applications:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford
Oxford Univ. Press
2013
|
| Ausgabe: | 1. ed |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | XIII, 702 S. Ill., graph. Darst. |
| ISBN: | 9780199695607 0199695601 |
Internformat
MARC
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| 245 | 1 | 0 | |a Bayesian theory and applications |c ed. by Paul Damien ... |
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Datensatz im Suchindex
| _version_ | 1804150010945732608 |
|---|---|
| adam_text | Contents
Contributors
χ
Introduction
xii
Part I Exchangeability
1
Observables
and models: exchangeability and the
inductive argument
з
Michael Goldstein
2
Exchangeability and its ramifications
19
A. Philip
Dawid
Part II Hierarchical Models
3
Hierarchical modelling
33
Alan E. Gelfand and Souparno Ghosh
4
Bayesian hierarchical kernel machines for nonlinear
regression and classification
50
Sounak Chakraborty,
Bani K.
Mallick and Malay Ghosh
5
Flexible Bayesian modelling for clustered categorical
responses in developmental toxicology
70
Athanasios
Kottas
and Kassandra Fronczyk
Part III Markov Chain Monte Carlo
6
Markov chain Monte Carlo methods
87
Siddhartha Chib
7
Advances in Markov chain Monte Carlo
104
Jim E. Griffin and David A. Stephens
Part IV Dynamic Models
8
Bayesian dynamic modelling
нѕ
Mike West
9
Hierarchical modelling in time series: the factor
analytic approach
167
Dani Gamerman
and Esther Salazar
viii
I Contents
10
Dynamic and spatial modelling of block maxima extremes
183
Gabriel Huerta
and Glenn A. Stark
Part V Sequential Monte Carlo
11
Online Bayesian learning in dynamic models: an illustrative
introduction to particle methods
203
Hedibert F. Lopes and Carlos M.
Carvalho
12
Semi-supervised classification of texts using particle learning
for probabilistic automata
229
Ana Paula Sales, Christopher Challis, Ryan Prenger and Daniel
Meri
Part VI Nonparametrics
13
Bayesian nonparametrics
249
Stephen G. Walker
14
Geometric weight priors and their applications
271
Ramsés
H. Mena
15
Revisiting Bayesian curve fitting using multivariate
normal mixtures
297
Stephen G. Walker and George Karabatsos
Part
VII
Spline Models and Copulas
16
Applications of Bayesian smoothing splines
309
Sally Wood
17
Bayesian approaches to copula modelling
336
Michael Stanley Smith
Part
VIII
Model Elaboration and Prior Distributions
18
Hypothesis testing and model uncertainty
361
M. J. Bayarri andj.
O. Berger
19
Proper and non-informative conjugate priors for exponential
family models 395
E.
Gutiérrez-Peña
and M.
Mendoza
20
Bayesian model specification: heuristics and examples 4o9
David Draper
21
Case studies in Bayesian screening for time-varying model
structure: the partition problem 432.
Zesong Liu, Jesse
Windle
and James G. Scott
Part IX Regressions and Model Averaging
22
Bayesian regression structure discovery 451
Hugh A.
Chipman,
Edward I. George and Robert E. McCulloch
Contents
I
ix
23
Gibbs sampling for ordinary, robust and logistic regression
with Laplace priors
466
Robert B, Gramacy
24
Bayesian model averaging in the M-open framework
483
Merlise Clyde and Edwin S. Iversen
Part X Finance and Actuarial Science
25
Asset allocation in finance: a Bayesian perspective
soi
Eric Jacquier
and Nicholas G. Poison
26
Markov chain Monte Carlo methods in corporate finance
516
Arthur Korteweg
27
Actuarial credibility theory and Bayesian statistics—the story
of a special evolution
546
Udi Makov
Part XI Medicine and Biostatistics
28
Bayesian models in biostatistics and medicine
557
Peter
Müller
29
Subgroup analysis
576
Purushottam W. Laud, Siva Sivaganesan and Peter
Müller
30
Surviving fully Bayesian nonparametric regression models
593
Timothy E. Hanson and
Alejandro Jara
Part
XII
Inverse Problems and Applications
31
Inverse problems
619
Colin Fox,
Heikki
Haario and J. Andres Christen
32
Approximate marginalization over modelling errors and
uncertainties in inverse problems
644
Jari
Kaipio and
Ville Kolehmainen
33
Bayesian reconstruction of particle beam phase space
673
С
Nakhleh, D. Higdon, C. K. Allen and R. Ryne
Adrian Smith s research supervision (PhD)
687
Adrian Smith s publications
689
Index
697
The development of hierarchical models and Markov chain Monte Carlo
techniques are some of the most profound advances in Bayesian analysis in the
past
40
years and provide the basis for advances in virtually all areas of applied and
theoretical Bayesian statistics. This volume honours the contributions of Sir Adrian
Smith, one of the seminal Bayesian researchers, with his papers on Hierarchical
Models, Sequential Monte Carlo, and Markov chain Monte Carlo, and his
mentoring of numerous graduate students. The chapters in this book, authored by
prominent statisticians influenced by Adrian, guide the reader along a statistical
journey that starts with the basic structure of Bayesian theory, and then provides
details on most of the past and present advances in this field.
The book has a unique format.There is an explanatory chapter devoted to each
conceptual advance followed by journal-style chapters that provide applications
or further advances on the concept. Thus, the volume is both a textbook and a
compendium of papers covering a vast range of topics. It is appropriate for a
well-informed novice interested in understanding the basic approach, methods
and recent applications. Due to its advanced chapters and reviews of recent work,
it is also appropriate for a more mature reader interested in recent applications and
developments, and who may be looking for ideas that could spawn new research.
This unique book is aimed at academics and practitioners, and will be a
useful resource for undergraduate and graduate students in statistics, medicine,
engineering, scientific computation, business, psychology, bioinformatics,
computational physics, graphical models, neural networks, geosciences, and
public policy.
|
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| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| edition | 1. ed |
| format | Book |
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| isbn | 9780199695607 0199695601 |
| language | English |
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| physical | XIII, 702 S. Ill., graph. Darst. |
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| spelling | Bayesian theory and applications ed. by Paul Damien ... 1. ed Oxford Oxford Univ. Press 2013 XIII, 702 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 s DE-604 Damien, Paul edt Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025686577&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025686577&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Bayesian theory and applications Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
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| title | Bayesian theory and applications |
| title_auth | Bayesian theory and applications |
| title_exact_search | Bayesian theory and applications |
| title_full | Bayesian theory and applications ed. by Paul Damien ... |
| title_fullStr | Bayesian theory and applications ed. by Paul Damien ... |
| title_full_unstemmed | Bayesian theory and applications ed. by Paul Damien ... |
| title_short | Bayesian theory and applications |
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