Bayesian non‐parametric hidden Markov models with applications in genomics
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- 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.
- Jason Ng & Catherine S. Forbes & Gael M. Martin & Brendan P.M. McCabe, 2011. "Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models," Monash Econometrics and Business Statistics Working Papers 11/11, Monash University, Department of Econometrics and Business Statistics.
- 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).
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"Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates,"
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Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
- Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," MPRA Paper 39023, University Library of Munich, Germany.
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- 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).
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KeywordsBlock Gibbs sampler ; Copy number variation ; Local and global clustering ; Partial exchangeability ; Partition models ; Retrospective sampling ;
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