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Bayesian non‐parametric hidden Markov models with applications in genomics

Author Info

Listed author(s):
  • C. Yau
  • O. Papaspiliopoulos
  • G. O. Roberts
  • C. Holmes

Abstract

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File URL: http://hdl.handle.net/10.1111/j.1467-9868.2010.00756.x
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Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Volume (Year): 73 (2011)
Issue (Month): 1 (January)
Pages: 37-57

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Handle: RePEc:bla:jorssb:v:73:y:2011:i:1:p:37-57
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Keywords: Block Gibbs sampler ; Copy number variation ; Local and global clustering ; Partial exchangeability ; Partition models ; Retrospective sampling ;

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Cited by:

  1. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
  2. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
  3. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
  4. repec:dau:papers:123456789/13438 is not listed on IDEAS
  5. Chopin, Nicolas & Gadat, Sébastien & Guedj, Benjamin & Guyader, Arnaud & Vernet, Elodie, 2015. "On some recent advances in high dimensional Bayesian Statistics," TSE Working Papers 15-557, Toulouse School of Economics (TSE).
  6. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "On the stick–breaking representation of normalized inverse Gaussian priors," DEM Working Papers Series 008, University of Pavia, Department of Economics and Management.

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