Bayesian essentials with R:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY [u.a.]
Springer
2014
|
| Ausgabe: | 2. ed. |
| Schriftenreihe: | Springer texts in statistics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 1. Aufl. u.d.T.: Marin, Jean-Michel: Bayesian core |
| Beschreibung: | XIV, 296 S. Ill., graph. Darst. |
| ISBN: | 9781461486862 |
Internformat
MARC
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| 008 | 131010s2014 ad|| |||| 00||| eng d | ||
| 020 | |a 9781461486862 |c hbk |9 978-1-4614-8686-2 | ||
| 035 | |a (OCoLC)895935151 | ||
| 035 | |a (DE-599)BVBBV041352153 | ||
| 040 | |a DE-604 |b ger |e rakwb | ||
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| 100 | 1 | |a Marin, Jean-Michel |4 aut | |
| 245 | 1 | 0 | |a Bayesian essentials with R |c Jean M. Marin ; Christian P. Robert |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a New York, NY [u.a.] |b Springer |c 2014 | |
| 300 | |a XIV, 296 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Springer texts in statistics | |
| 500 | |a 1. Aufl. u.d.T.: Marin, Jean-Michel: Bayesian core | ||
| 650 | 0 | 7 | |a Bayes-Entscheidungstheorie |0 (DE-588)4144220-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Numerisches Verfahren |0 (DE-588)4128130-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |D s |
| 689 | 0 | 1 | |a Bayes-Entscheidungstheorie |0 (DE-588)4144220-9 |D s |
| 689 | 0 | 2 | |a Numerisches Verfahren |0 (DE-588)4128130-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Robert, Christian P. |d 1961- |0 (DE-588)115436448 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-4614-8687-9 |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026800705&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-026800705 | ||
Datensatz im Suchindex
| DE-BY-862_location | 2000 |
|---|---|
| DE-BY-FWS_call_number | 2000/SK 830 M337(2) |
| DE-BY-FWS_katkey | 653116 |
| DE-BY-FWS_media_number | 083000517957 |
| _version_ | 1806176845872234496 |
| adam_text | Contents
User s
Manual.............................................
I
1.1
Expectations
............................................ 2
1.2
Prerequisites and Further Heading
......................... 3
1.3
Styles and Fonts
......................................... 1
1.4
An Introduction to
R
.................................... 5
1.1.1
Getting Started
...................................
(i
1.4.2
R
Objects
........................................ 8
1.4.3
Probability Distributions in
R
....................... 15
1.4.1
Graphical Facilities
................................ lü
і
.1.5
Writing New
R
Functions
........................... 19
1.4.0
Input and Output in
R
............................. 21
1.4.7
Administration of
R
Objects
........................ 21
1.5
The bayess Package
...................................... 22
Normal Models
............................................ 25
2.1
Normal Modeling
........................................ 26
2.2
The Bayesian Toolkit
.................................... 28
2.2.
1 Posterior Distribution
.............................. 28
2.2.2
Bayesian Estimates
................................ 33
2.2.3
Conjugate Prior Distributions
....................... 31
2.2.4
Non
info
ima
t
ivo
Priors
............................. 35
2.2.5
Bayesian Credible Interval«
......................... 37
2.3
Bayesian Model Choice
................................... 38
2.3.1
The Model Index as
a Paramotor
.................... 39
2.3.2
The
Bayes
Factor
................................. 41
2.3.3
The Ban on Improper Prior«
........................ 43
2.4
Monte Carlo Methods
.................................... 40
2.4.1
An Approximation Based on Simulations
............. 47
2.4.2
Importance
Sampling
.............................. 49
2.4.3
Approximation of
Bayes
Factors
..................... 52
XJ
xii Contents
2.5
Outlier Detection
........................................ 58
2.6
Exerciser
............................................... 61
3
Regression and Variable Selection
......................... 65
3.1
Linear Model«
........................................... 66
3.2
Classical Least Squares Estimator
......................... 69
3.3
The Jeffreys Prior Analysis
............................... 73
3.4 Zellners
G-Prior
Analysis
................................ 74
3.4.1
A Semi-iionmforniative Solution
..................... 75
3.4.2
The BayesReg
R
Function
........................... 80
3.4.3
Bayes
Fai-tors
and Model Comparison
................ 81
3.4.4
Prediction
........................................ 84
3.5
Markov Chain Monte Carlo Methods
....................... 85
3.5.1
Conditionals
...................................... 86
3.5.2
Two-Stage Gibbs Sampler
.......................... 87
3.5.3
The General Gibbs Sampler
........................ 90
3.6
Variable Selection
....................................... 91
3.6.1
Deciding on Explanatory Variables
.................. 91
3.6.2
G-Prior Distributions for Model Choice
.............. 93
3.6.3
A Stochastic Search for the Most Likely Model
........ 96
3.7
Exercises
............................................... 98
4
Generalized Linear Models
................................103
4.1
A Generalization of the Linear Model
......................104
4.1.1
Motivation
.......................................104
4.1.2
Link Functions
....................................106
4.2
Metropolis Hastings Algorithms
...........................108
4.2.1
Definition
........................................109
4.2.2
The Independence Sampler
.........................110
4.2.3
The Random Walk Sampler
........................
Ill
4.2.4
Output Analysis and Proposal Design
................
Ill
4.3
The
Probit
Model
.......................................115
4.3.1
Flat Prior
........................................115
4.3.2
Noninformative G-Priors
...........................117
4.3.3
About Informative Prior Analyses
...................122
4.4
The Logit Model
........................................124
4.5
Log-Linear Models
.......................................127
4.5.1
Contingency Tables
................................127
4.5.2
Inference Under a Flat Prior
........................131
4.5.3
Model Choice and Significance of the Parameters
......133
4.6
Exercises
...............................................137
Contents xiii
5
Capture—Recapture
Experiments..........................139
5.1
Inference in a Finite Population
...........................140
5.2
Sampling Models
........................................142
5.2.1
The Binomial Capture Model
.......................142
5.2.2
The Two-Stage Capture Recapture Model
............143
5.2.3
The
Т
-Stage
Capture Recapture Model
..............148
5.3
Open Populations
.......................................152
5.4
Accept--Reject Algorithms
................................156
5.5
The
Amason
Schwarz
Capture Recapture Model
............160
5.5.1
Modeling
.........................................161
5.5.2
Gibbs Sampler
....................................165
5.6
Exercises
...............................................168
6
Mixture Models
...........................................173
6.1
Missing Variable Models
..................................174
6.2
Finite Mixture Models
...................................176
6.3
Mixture Likelihoods and Posteriors
........................177
6.4
MCMC Solutions
........................................182
6.5
Label Switching Difficulty
................................192
6.6
Prior Selection
..........................................198
6.7
Tempering
..............................................199
6.8
Mixtures with an Unknown Number of Components
.........201
6.9
Exorcises
...............................................206
7
Time Series
................................................209
7.1
Time-Indexed Data
......................................210
7.1.1
Setting
...........................................210
7.1.2
Stability of Time Series
............................212
7.2 Autoregressive (AR)
Models
..............................214
7.2.1
The Models
.......................................215
7.2.2
Exploring the Parameter Space by MCMC
Algorithms
.......................................219
7.3
Moving Average (MA) Models
............................226
7.4
ARMA
Models and Other Extensions
......................232
7.5
Hidden Markov Models
...................................236
7.5.1
Basics
...........................................237
7.5.2
Forward Backward Representation
..................241
7.6
Exercises
...............................................248
8
Image Analysis
............................................251
8.1
Image Analysis as a Statistical Problem
....................252
8.2
Spatial Dependence
......................................252
8.2.1
Grids and Lattices
.................................252
8.2.2
Markov Random Fields
............................254
8.2.3
The Ising Model
..................................256
8.2.4
The Potts Model
..................................260
xiv
Contents
8.3
Handling
tl
io
Normalizing Constant
........................
2G2
8.3.1
Path Sampling
....................................20 1
8.3.2
The ABC Method
.................................207
8.3.3
Inference on Potts Models
..........................270
8.4
Image Segmentation
.....................................273
8.5
Exercises
...............................................281
About the Authors
............................................285
References
.....................................................287
Index
..........................................................2!) 1
|
| any_adam_object | 1 |
| author | Marin, Jean-Michel Robert, Christian P. 1961- |
| author_GND | (DE-588)115436448 |
| author_facet | Marin, Jean-Michel Robert, Christian P. 1961- |
| author_role | aut aut |
| author_sort | Marin, Jean-Michel |
| author_variant | j m m jmm c p r cp cpr |
| building | Verbundindex |
| bvnumber | BV041352153 |
| classification_rvk | QH 233 SK 830 SK 850 |
| ctrlnum | (OCoLC)895935151 (DE-599)BVBBV041352153 |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV041352153 |
| illustrated | Illustrated |
| indexdate | 2024-08-01T11:27:47Z |
| institution | BVB |
| isbn | 9781461486862 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026800705 |
| oclc_num | 895935151 |
| open_access_boolean | |
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| owner_facet | DE-521 DE-11 DE-473 DE-BY-UBG DE-945 DE-862 DE-BY-FWS DE-83 DE-29 |
| physical | XIV, 296 S. Ill., graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer texts in statistics |
| spellingShingle | Marin, Jean-Michel Robert, Christian P. 1961- Bayesian essentials with R Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Numerisches Verfahren (DE-588)4128130-5 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| subject_GND | (DE-588)4144220-9 (DE-588)4128130-5 (DE-588)4204326-8 |
| title | Bayesian essentials with R |
| title_auth | Bayesian essentials with R |
| title_exact_search | Bayesian essentials with R |
| title_full | Bayesian essentials with R Jean M. Marin ; Christian P. Robert |
| title_fullStr | Bayesian essentials with R Jean M. Marin ; Christian P. Robert |
| title_full_unstemmed | Bayesian essentials with R Jean M. Marin ; Christian P. Robert |
| title_short | Bayesian essentials with R |
| title_sort | bayesian essentials with r |
| topic | Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd Numerisches Verfahren (DE-588)4128130-5 gnd Bayes-Verfahren (DE-588)4204326-8 gnd |
| topic_facet | Bayes-Entscheidungstheorie Numerisches Verfahren Bayes-Verfahren |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026800705&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT marinjeanmichel bayesianessentialswithr AT robertchristianp bayesianessentialswithr |
Inhaltsverzeichnis
Sonderstandort Fakultät
| Signatur: |
2000 SK 830 M337(2) |
|---|---|
| Exemplar 1 | nicht ausleihbar Checked out – Rückgabe bis: 31.12.2099 Vormerken |