A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data
暂无分享,去创建一个
[1] D. N. Geary. Mixture Models: Inference and Applications to Clustering , 1989 .
[2] O. Cappé,et al. Reversible jump, birth‐and‐death and more general continuous time Markov chain Monte Carlo samplers , 2003 .
[3] Ajay Jasra,et al. Population-Based Reversible Jump Markov Chain Monte Carlo , 2007, 0711.0186.
[4] Luc Martens,et al. The Signal Tandmobiel (r) Project: a longitudinal intervention oral health promotion study in Flanders (Belgium) baseline and first year results. , 2000 .
[5] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[6] C. Robert,et al. Estimation of Finite Mixture Distributions Through Bayesian Sampling , 1994 .
[7] P. Green,et al. Corrigendum: On Bayesian analysis of mixtures with an unknown number of components , 1997 .
[8] A. F. Smith,et al. Statistical analysis of finite mixture distributions , 1986 .
[9] W. Wong,et al. The calculation of posterior distributions by data augmentation , 1987 .
[10] A. Jasra,et al. Population-based reversible jump Markov chain , 2005 .
[11] M. Plummer. Penalized loss functions for Bayesian model comparison. , 2008, Biostatistics.
[12] Scott A. Sisson,et al. Transdimensional Markov Chains , 2005 .
[13] J. Christiansen,et al. Time and duration of eruption of first and second permanent molars: a longitudinal investigation. , 2003, Community dentistry and oral epidemiology.
[14] K. Roeder. Density estimation with confidence sets exemplified by superclusters and voids in the galaxies , 1990 .
[15] Petros Dellaportas,et al. Multivariate mixtures of normals with unknown number of components , 2006, Stat. Comput..
[16] Marco Alfò,et al. Advances in Mixture Models , 2007, Comput. Stat. Data Anal..
[17] Andrew Thomas,et al. WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..
[18] C. Robert,et al. Computational and Inferential Difficulties with Mixture Posterior Distributions , 2000 .
[19] W. Wong,et al. Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models , 2001 .
[20] M. Stephens. Bayesian analysis of mixture models with an unknown number of components- an alternative to reversible jump methods , 2000 .
[21] David J. Lunn,et al. Generic reversible jump MCMC using graphical models , 2009, Stat. Comput..
[22] M. Stephens. Dealing with label switching in mixture models , 2000 .
[23] C. Robert,et al. Deviance information criteria for missing data models , 2006 .
[24] Ajay Jasra,et al. Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling , 2005 .
[25] P. Green,et al. On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .
[26] Radford M. Neal. Sampling from multimodal distributions using tempered transitions , 1996, Stat. Comput..
[27] L. Wasserman,et al. Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .
[28] Bradley P. Carlin,et al. Bayesian measures of model complexity and fit , 2002 .
[29] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .
[30] John Geweke,et al. Efficient Simulation from the Multivariate Normal and Student-t Distributions Subject to Linear Constraints and the Evaluation of Constraint Probabilities , 1991 .