de Finetti Priors using Markov chain Monte Carlo computations

被引：0
|
作者：
Sergio Bacallado
Persi Diaconis
Susan Holmes
机构：
[1] Stanford University,Sequoia Hall
来源：
Statistics and Computing | 2015年 / 25卷
关键词：
Priors; MCMC; Contingency tables; Bayesian inference; Independence; 62C10; 62C25;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Recent advances in Monte Carlo methods allow us to revisit work by de Finetti who suggested the use of approximate exchangeability in the analyses of contingency tables. This paper gives examples of computational implementations using Metropolis Hastings, Langevin, and Hamiltonian Monte Carlo to compute posterior distributions for test statistics relevant for testing independence, reversible or three-way models for discrete exponential families using polynomial priors and Gröbner bases.
引用
收藏
页码：797 / 808
页数：11
相关论文
共 50 条
  • [1] de Finetti Priors using Markov chain Monte Carlo computations
    Bacallado, Sergio
    Diaconis, Persi
    Holmes, Susan
    [J]. STATISTICS AND COMPUTING, 2015, 25 (04) : 797 - 808
  • [2] Bayesian object matching with hierarchical priors and Markov chain Monte Carlo
    Tamminen, T
    Lampinen, J
    [J]. BAYESIAN STATISTICS 7, 2003, : 691 - 700
  • [3] Markov chain Monte Carlo using an approximation
    Christen, JA
    Fox, C
    [J]. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2005, 14 (04) : 795 - 810
  • [4] Detection and estimation of signals by reversible jump Markov Chain Monte Carlo computations
    Djuric, PM
    Godsill, SJ
    Fitzgerald, WJ
    Rayner, PJW
    [J]. PROCEEDINGS OF THE 1998 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-6, 1998, : 2269 - 2272
  • [5] Markov Chain Monte Carlo
    Henry, Ronnie
    [J]. EMERGING INFECTIOUS DISEASES, 2019, 25 (12) : 2298 - 2298
  • [6] Using Markov Chain Monte Carlo to Play Trivia
    Deutch, Daniel
    Greenshpan, Ohad
    Kostenko, Boris
    Milo, Tova
    [J]. IEEE 27TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2011), 2011, : 1308 - 1311
  • [7] Pairwise clustering using a Monte Carlo Markov Chain
    Stosic, Borko D.
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2009, 388 (12) : 2373 - 2382
  • [8] Segmentation Using Improved Markov Chain Monte Carlo
    Wang, Xiangrong
    Wang, Ming
    Pei, JiaLi
    [J]. 2011 INTERNATIONAL CONFERENCE ON FUTURE COMPUTER SCIENCE AND APPLICATION (FCSA 2011), VOL 2, 2011, : 172 - 175
  • [9] Alignment and correspondence using Markov Chain Monte Carlo
    Moss, S
    Hancock, ER
    [J]. 15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 1, PROCEEDINGS: COMPUTER VISION AND IMAGE ANALYSIS, 2000, : 928 - 931
  • [10] Population Markov Chain Monte Carlo
    Laskey, KB
    Myers, JW
    [J]. MACHINE LEARNING, 2003, 50 (1-2) : 175 - 196
12345