Introduction to probability simulation and Gibbs sampling with R:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Springer
2010
|
| Schriftenreihe: | Use R!
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 307 S. Ill., graph. Darst. |
| ISBN: | 9780387402734 |
Internformat
MARC
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| 100 | 1 | |a Suess, Eric A. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Introduction to probability simulation and Gibbs sampling with R |c Eric A. Suess ; Bruce E. Trumbo |
| 264 | 1 | |a New York [u.a.] |b Springer |c 2010 | |
| 300 | |a XIII, 307 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 689 | 2 | |5 DE-604 | |
| 700 | 1 | |a Trumbo, Bruce E. |e Verfasser |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-0-387-68765-0 |
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Datensatz im Suchindex
| _version_ | 1804142788366827520 |
|---|---|
| adam_text | Contents
Preface
.....................................................
VII
1
Introductory Examples: Simulation, Estimation, and
Graphics
................................................... 1
1.1
Simulating Random Samples from Finite Populations
........ 1
1.2
Coverage Probabilities of Binomial Confidence Intervals
...... 8
1.3
Problems
............................................... 15
2
Generating Random Numbers
............................. 23
2.1
Introductory Comments on Random Numbers
............... 23
2.2
Linear Congruential Generators
........................... 24
2.3
Validating Desirable Properties of a Generator
.............. 27
2.4
Transformations of Uniform Random Variables
.............. 31
2.5
Transformations Involving Normal Random Variables
........ 36
2.6
Problems
............................................... 39
3
Monte Carlo Integration and Limit Theorems
............. 51
3.1
Computing Probabilities of Intervals
....................... 51
3.2
Convergence
—
The Law of Large Numbers
.................. 56
3.3
Central Limit Theorem
................................... 64
3.4
A Closer Look at Monte Carlo Integration
.................. 67
3.5
When Is Simulation Appropriate?
......................... 71
3.6
Problems
............................................... 73
4
Sampling from Applied Probability Models
................ 87
4.1
Models Based on Exponential Distributions
................. 87
4.2
Range of a Normal Sample
............................... 93
4.3
Joint Distribution of the Sample
Mean and Standard Deviation
............................ 95
4.4
Nonparametric Bootstrap Distributions
.................... 101
4.5
Problems
............................................... 105
XII Contents
5
Screening Tests
............................................119
5.1
Prevalence, Sensitivity, and Specificity
.....................119
5.2
An Attempt to Estimate Prevalence
.......................122
5.3
Predictive Values
........................................126
5.4
Bayes
Theorem for Events
...............................130
5.5
Problems
...............................................131
6
Markov Chains with Two States
...........................139
6.1
The Markov Property
....................................140
6.2
Transition Matrices
......................................146
6.3
Limiting Behavior of a 2-State Chain
......................148
6.4
A Simple Gibbs Sampler
.................................150
6.5
Problems
...............................................154
7
Examples of Markov Chains with Larger State Spaces
.....159
7.1
Properties of ¿f-State Chains
.............................159
7.2
Computing Long-Run Distributions
........................161
7.3
Countably Infinite State Spaces
...........................166
7.4
Continuous State Spaces
.................................169
7.5
Uses of Markov Chains in Computation
....................176
7.6
Problems
...............................................181
8
Introduction to Bayesian Estimation
.......................195
8.1
Prior Distributions
......................................196
8.2
Data and Posterior Distributions
..........................200
8.3
Problems
...............................................206
9
Using Gibbs Samplers to Compute Bayesian
Posterior Distributions
....................................219
9.1
Bayesian Estimates of Disease Prevalence
...................219
9.2
Bayesian Estimates of Normal Mean and Variance
...........224
9.3
Bayesian Estimates of Components of Variance
..............229
9.4
Problems
...............................................237
10
Using WinBUGS for Bayesian Estimation
.................249
10.1
What Is BUGS?
.........................................250
10.2
Running WinBUGS: The Binomial Proportion
..............251
10.3
Running WinBUGS with a Script: Two-Sample Problem
.....255
10.4
Running WinBUGS and MCMC Diagnostics:
Variance Components
....................................261
10.5
A Final WinBUGS Example: Linear Regression
.............266
10.6
Further Uses of WinBUGS
................................270
10.7
Problems
...............................................270
Contents XIII
11 Appendix:
Getting Started with
R.........................
275
11.1
Basics
..................................................275
11.1.1
Installation
.......................................275
11.1.2
Using
R
as a Calculator
............................276
11.1.3
Defining a Vector
..................................276
11.2
Using Vectors
...........................................279
11.2.1
Simple Arithmetic with Vectors
.....................280
11.2.2
Indexes and Assignments
...........................281
11.2.3
Vector Functions
..................................282
11.2.4
Comparisons of Vectors
............................283
11.3
Exploring Infinite Sequences
..............................285
11.4
Loops
..................................................288
11.5
Graphical Functions
.....................................290
11.6
Sampling from a Finite Population
........................294
References
.....................................................301
Index
..........................................................305
|
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| discipline | Informatik Soziologie Mathematik Wirtschaftswissenschaften |
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| illustrated | Illustrated |
| indexdate | 2024-07-09T22:37:20Z |
| institution | BVB |
| isbn | 9780387402734 |
| language | English |
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| physical | XIII, 307 S. Ill., graph. Darst. |
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| spelling | Suess, Eric A. Verfasser aut Introduction to probability simulation and Gibbs sampling with R Eric A. Suess ; Bruce E. Trumbo New York [u.a.] Springer 2010 XIII, 307 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Use R! Wahrscheinlichkeit (DE-588)4137007-7 gnd rswk-swf Simulation (DE-588)4055072-2 gnd rswk-swf Stichprobennahme (DE-588)4183250-4 gnd rswk-swf Gibbs-sampling (DE-588)4352359-6 gnd rswk-swf Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf R Programm (DE-588)4705956-4 s Simulation (DE-588)4055072-2 s Wahrscheinlichkeit (DE-588)4137007-7 s DE-604 Gibbs-sampling (DE-588)4352359-6 s Stichprobennahme (DE-588)4183250-4 s Wahrscheinlichkeitsverteilung (DE-588)4121894-2 s Trumbo, Bruce E. Verfasser aut Erscheint auch als Online-Ausgabe 978-0-387-68765-0 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020198938&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Suess, Eric A. Trumbo, Bruce E. Introduction to probability simulation and Gibbs sampling with R Wahrscheinlichkeit (DE-588)4137007-7 gnd Simulation (DE-588)4055072-2 gnd Stichprobennahme (DE-588)4183250-4 gnd Gibbs-sampling (DE-588)4352359-6 gnd Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4137007-7 (DE-588)4055072-2 (DE-588)4183250-4 (DE-588)4352359-6 (DE-588)4121894-2 (DE-588)4705956-4 |
| title | Introduction to probability simulation and Gibbs sampling with R |
| title_auth | Introduction to probability simulation and Gibbs sampling with R |
| title_exact_search | Introduction to probability simulation and Gibbs sampling with R |
| title_full | Introduction to probability simulation and Gibbs sampling with R Eric A. Suess ; Bruce E. Trumbo |
| title_fullStr | Introduction to probability simulation and Gibbs sampling with R Eric A. Suess ; Bruce E. Trumbo |
| title_full_unstemmed | Introduction to probability simulation and Gibbs sampling with R Eric A. Suess ; Bruce E. Trumbo |
| title_short | Introduction to probability simulation and Gibbs sampling with R |
| title_sort | introduction to probability simulation and gibbs sampling with r |
| topic | Wahrscheinlichkeit (DE-588)4137007-7 gnd Simulation (DE-588)4055072-2 gnd Stichprobennahme (DE-588)4183250-4 gnd Gibbs-sampling (DE-588)4352359-6 gnd Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Wahrscheinlichkeit Simulation Stichprobennahme Gibbs-sampling Wahrscheinlichkeitsverteilung R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020198938&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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