語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
An introduction to statistical compu...
~
Voss, Jochen.
An introduction to statistical computing :a simulation-based approach /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
An introduction to statistical computing :Jochen Voss, School of Mathematics, University of Leeds, UK.
其他題名:
a simulation-based approach /
作者:
Voss, Jochen.
出版者:
Chichester, West Sussex :Wiley,2014.
面頁冊數:
xii, 382 p. :ill. ;24 cm.
標題:
Mathematical statisticsData processing.
電子資源:
http://catalogimages.wiley.com/images/db/jimages/9781118357729.jpg
ISBN:
1118357728 (hbk.) :
An introduction to statistical computing :a simulation-based approach /
Voss, Jochen.
An introduction to statistical computing :
a simulation-based approach /Jochen Voss, School of Mathematics, University of Leeds, UK. - Chichester, West Sussex :Wiley,2014. - xii, 382 p. :ill. ;24 cm. - Wiley series in computational statistics.
Includes bibliographical references (p. 375-377) and index.
Contents note continued: 3.2.2. Monte Carlo error -- 3.2.3. Choice of sample size -- 3.2.4. Refined error bounds -- 3.3. Variance reduction methods -- 3.3.1. Importance sampling -- 3.3.2. Antithetic variables -- 3.3.3. Control variates -- 3.4. Applications to statistical inference -- 3.4.1. Point estimators -- 3.4.2. Confidence intervals -- 3.4.3. Hypothesis tests -- 3.5. Summary and further reading -- Exercises -- 4. Markov Chain Monte Carlo methods -- 4.1. The Metropolis-Hastings method -- 4.1.1. Continuous state space -- 4.1.2. Discrete state space -- 4.1.3. Random walk Metropolis sampling -- 4.1.4. The independence sampler -- 4.1.5. Metropolis-Hastings with different move types -- 4.2. Convergence of Markov Chain Monte Carlo methods -- 4.2.1. Theoretical results -- 4.2.2. Practical considerations -- 4.3. Applications to Bayesian inference -- 4.4. The Gibbs sampler -- 4.4.1. Description of the method -- 4.4.2. Application to parameter estimation -- 4.4.3. Applications to image processing.
"This is a book about exploring random systems using computer simulation and thus, this book combines two different topic areas which have always fascinated me: the mathematical theory of probability and the art of programming computers"--
ISBN: 1118357728 (hbk.) :NT$2402
LCCN: 2013019321Subjects--Topical Terms:
183916
Mathematical statistics
--Data processing.
LC Class. No.: QA276.4 / .V66 2014
Dewey Class. No.: 519.501/13
An introduction to statistical computing :a simulation-based approach /
LDR
:05075cam a2200277 a 4500
001
458571
005
20150428170832.0
008
151012s2014 enka b 001 0 eng
010
$a
2013019321
020
$a
1118357728 (hbk.) :
$c
NT$2402
020
$a
9781118357729 (hbk.)
035
$a
17777306
040
$a
DLC
$b
eng
$c
DLC
$d
DLC
042
$a
pcc
050
0 0
$a
QA276.4
$b
.V66 2014
082
0 0
$a
519.501/13
$2
23
100
1
$a
Voss, Jochen.
$3
699005
245
1 3
$a
An introduction to statistical computing :
$b
a simulation-based approach /
$c
Jochen Voss, School of Mathematics, University of Leeds, UK.
260
$a
Chichester, West Sussex :
$b
Wiley,
$c
2014.
300
$a
xii, 382 p. :
$b
ill. ;
$c
24 cm.
490
0
$a
Wiley series in computational statistics
504
$a
Includes bibliographical references (p. 375-377) and index.
505
0
$a
Contents note continued: 3.2.2. Monte Carlo error -- 3.2.3. Choice of sample size -- 3.2.4. Refined error bounds -- 3.3. Variance reduction methods -- 3.3.1. Importance sampling -- 3.3.2. Antithetic variables -- 3.3.3. Control variates -- 3.4. Applications to statistical inference -- 3.4.1. Point estimators -- 3.4.2. Confidence intervals -- 3.4.3. Hypothesis tests -- 3.5. Summary and further reading -- Exercises -- 4. Markov Chain Monte Carlo methods -- 4.1. The Metropolis-Hastings method -- 4.1.1. Continuous state space -- 4.1.2. Discrete state space -- 4.1.3. Random walk Metropolis sampling -- 4.1.4. The independence sampler -- 4.1.5. Metropolis-Hastings with different move types -- 4.2. Convergence of Markov Chain Monte Carlo methods -- 4.2.1. Theoretical results -- 4.2.2. Practical considerations -- 4.3. Applications to Bayesian inference -- 4.4. The Gibbs sampler -- 4.4.1. Description of the method -- 4.4.2. Application to parameter estimation -- 4.4.3. Applications to image processing.
505
0
$a
Contents note continued: 4.5. Reversible Jump Markov Chain Monte Carlo -- 4.5.1. Description of the method -- 4.5.2. Bayesian inference for mixture distributions -- 4.6. Summary and further reading -- 4.6. Exercises -- 5. Beyond Monte Carlo -- 5.1. Approximate Bayesian Computation -- 5.1.1. Basic Approximate Bayesian Computation -- 5.1.2. Approximate Bayesian Computation with regression -- 5.2. Resampling methods -- 5.2.1. Bootstrap estimates -- 5.2.2. Applications to statistical inference -- 5.3. Summary and further reading -- Exercises -- 6. Continuous-time models -- 6.1. Time discretisation -- 6.2. Brownian motion -- 6.2.1. Properties -- 6.2.2. Direct simulation -- 6.2.3. Interpolation and Brownian bridges -- 6.3. Geometric Brownian motion -- 6.4. Stochastic differential equations -- 6.4.1. Introduction -- 6.4.2. Stochastic analysis -- 6.4.3. Discretisation schemes -- 6.4.4. Discretisation error -- 6.5. Monte Carlo estimates -- 6.5.1. Basic Monte Carlo -- 6.5.2. Variance reduction methods.
505
0
$a
Contents note continued: 6.5.3. Multilevel Monte Carlo estimates -- 6.6. Application to option pricing -- 6.7. Summary and further reading -- Exercises -- Appendix A Probability reminders -- A.1. Events and probability -- A.2. Conditional probability -- A.3. Expectation -- A.4. Limit theorems -- A.5. Further reading -- Appendix B Programming in R -- B.1. General advice -- B.2.R as a Calculator -- B.2.1. Mathematical operations -- B.2.2. Variables -- B.2.3. Data types -- B.3. Programming principles -- B.3.1. Don’t repeat yourself! -- B.3.2. Divide and conquer! -- B.3.3. Test your code! -- B.4. Random number generation -- B.5. Summary and further reading -- Exercises -- Appendix C Answers to the exercises -- C.1. Answers for Chapter 1 -- C.2. Answers for Chapter 2 -- C.3. Answers for Chapter 3 -- C.4. Answers for Chapter 4 -- C.5. Answers for Chapter 5 -- C.6. Answers for Chapter 6 -- C.7. Answers for Appendix B.
505
0
$a
Pseudo random number generators -- 1.1.1. The linear congruential generator -- 1.1.2. Quality of pseudo random number generators -- 1.1.3. Pseudo random number generators in practice -- 1.2. Discrete distributions -- 1.3. The inverse transform method -- 1.4. Rejection sampling -- 1.4.1. Basic rejection sampling -- 1.4.2. Envelope rejection sampling -- 1.4.3. Conditional distributions -- 1.4.4. Geometric interpretation -- 1.5. Transformation of random variables -- 1.6. Special-purpose methods -- 1.7. Summary and further reading -- Exercises -- 2. Simulating statistical models -- 2.1. Multivariate normal distributions -- 2.2. Hierarchical models -- 2.3. Markov chains -- 2.3.1. Discrete state space -- 2.3.2. Continuous state space -- 2.4. Poisson processes -- 2.5. Summary and further reading -- Exercises -- 3. Monte Carlo methods -- 3.1. Studying models via simulation -- 3.2. Monte Carlo estimates -- 3.2.1.Computing Monte Carlo estimates.
520
$a
"This is a book about exploring random systems using computer simulation and thus, this book combines two different topic areas which have always fascinated me: the mathematical theory of probability and the art of programming computers"--
$c
Provided by publisher.
650
0
$a
Mathematical statistics
$x
Data processing.
$3
183916
856
4 2
$3
Cover image
$u
http://catalogimages.wiley.com/images/db/jimages/9781118357729.jpg
筆 0 讀者評論
全部
西方語文圖書區(四樓)
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
320000682957
西方語文圖書區(四樓)
1圖書
一般圖書
QA276.4 V969 2014
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://catalogimages.wiley.com/images/db/jimages/9781118357729.jpg
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入