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Variational approximations in Bayesian model selection for finite mixture distributions

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  • McGrory, C.A.
  • Titterington, D.M.
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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4KMYRPW-1/2/428635340ac2d823187a0c04164508c5
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 51 (2007)
    Issue (Month): 11 (July)
    Pages: 5352-5367

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    Handle: RePEc:eee:csdana:v:51:y:2007:i:11:p:5352-5367

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    Web page: http://www.elsevier.com/locate/csda

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    1. Berg, Andreas & Meyer, Renate & Yu, Jun, 2004. "Deviance Information Criterion for Comparing Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 107-20, January.
    2. C. P. Robert & T. Rydén & D. M. Titterington, 2000. "Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 57-75.
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    Cited by:
    1. Melnykov, Volodymyr & Melnykov, Igor, 2012. "Initializing the EM algorithm in Gaussian mixture models with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1381-1395.
    2. Naranjo, L. & Martín, J. & Pérez, C.J., 2014. "Bayesian binary regression with exponential power link," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 464-476.
    3. Ruth Fuentes–García & Ramsés Mena & Stephen Walker, 2010. "A Probability for Classification Based on the Dirichlet Process Mixture Model," Journal of Classification, Springer, vol. 27(3), pages 389-403, November.

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