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Doing Bayesian data analysis : a tutorial with R, JAGS, and stan / 2nd ed

Doing Bayesian data analysis : a tutorial with R, JAGS, and stan / 2nd ed (13회 대출)

자료유형
단행본
개인저자
Kruschke, John K.
서명 / 저자사항
Doing Bayesian data analysis : a tutorial with R, JAGS, and stan / John Kruschke.
판사항
2nd ed.
발행사항
Boston :   Academic Press,   2015.  
형태사항
xii, 759 p. : ill. ; 25 cm.
기타형태 저록
Online version:   Kruschke, John K.   Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan.   2nd ed.   Boston : Academic Press, 2015   9780124058880   (211009) 000045949417  
ISBN
9780124058880 (hbk)
일반주기
Online version: Kruschke, John K. Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan. 2nd ed. Boston : Academic Press, 2015 9780124058880
서지주기
Includes bibliographical references (p.737-745) and idnex.
일반주제명
Bayesian statistical decision theory. R (Computer program language).
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090 ▼a 519.542 ▼b K94d2
100 1 ▼a Kruschke, John K.
245 1 0 ▼a Doing Bayesian data analysis : ▼b a tutorial with R, JAGS, and stan / ▼c John Kruschke.
250 ▼a 2nd ed.
260 ▼a Boston : ▼b Academic Press, ▼c 2015.
300 ▼a xii, 759 p. : ▼b ill. ; ▼c 25 cm.
504 ▼a Includes bibliographical references (p.737-745) and idnex.
650 0 ▼a Bayesian statistical decision theory.
650 0 ▼a R (Computer program language).
776 0 8 ▼i Online version: ▼a Kruschke, John K. ▼t Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan. ▼b 2nd ed. ▼d Boston : Academic Press, 2015 ▼z 9780124058880 ▼w (211009) 000045949417
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고7층/ 청구기호 519.542 K94d2 등록번호 111737058 도서상태 대출가능 반납예정일 예약 서비스 B M

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저자소개

존 크러슈케(지은이)

크러슈케 박사는 미국 인디아나 블루밍턴에 있는 인디애나대학교에서 심리학 뇌과학과와 통계학과의 겸임교수로 있다. 그는 인디애나대학교에서 우수강의상을 여덟 차례나 수상했으며, 미국 국립 과학원에서 트롤랜드(Troland) 연구상을 받았고, 인디애나대학교로부터 레막(Remak) 특별 연구자상도 받았다. 그는 독자, 그리고 동물에게 친절하다.

정보제공 : Aladin

목차

1.) This Book s Organization: Read Me First!
Part 1.) The Basics: Parameters, Probability, Bayes Rule, and R
2.) Introduction: Models We Believe In
3.) What Is This Stuff Called Probability?
4.) Bayes Rule
Part 2.) All the Fundamentals Applied to Inferring a Binomial Proportion
5.) Inferring a Binomial Proportion via Exact Mathematical Analysis
6.) Inferring a Binomial Proportion via Grid Approximation
7.) Inferring a Binomial Proportion via the Metropolis Algorithm
8.) Inferring Two Binomial Proportions via Gibbs Sampling
9.) Bernoulli Likelihood with Hierarchical Prior
10.) Hierarchical Modeling and Model Comparison
11.) Null Hypothesis Significance Testing
12.) Bayesian Approaches to Testing a Point ("Null") Hypothesis
13.) Goals, Power, and Sample Size
Part 3.) Applied to the Generalized Linear Model
14.) Overview of the Generalized Linear Model
15.) Metric Predicted Variable on a Single Group
16.) Metric Predicted Variable with One Metric Predictor
17.) Metric Predicted Variable with Multiple Metric Predictors
18.) Metric Predicted Variable with One Nominal Predictor
19.) Metric Predicted Variable with Multiple Nominal Predictors
20.) Dichotomous Predicted Variable
21.) Ordinal Predicted Variable
22.) Contingency Table Analysis
23.) Tools in the Trunk
REFERENCES
INDEX

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