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Computational methods for mixture of Dirichlet process models. (English) Zbl 0918.62064

Dey, Dipak (ed.) et al., Practical nonparametric and semiparametric Bayesian statistics. New York, NY: Springer. Lect. Notes Stat., Springer-Verlag. 133, 23-43 (1998).
Summary: This chapter lays out the basic computational strategies for models based on mixtures of Dirichlet processes. I describe the basic algorithm and give advice on how to improve this algorithm through a collapse of the state space of the Markov chain and through blocking of variates for generation. The computational methods are illustrated with a beta-binomial example and with the bioassay problem. Some advice is given for dealing with models that have little or no conjugacy present.
For the entire collection see [Zbl 0893.00018].

MSC:

62M05 Markov processes: estimation; hidden Markov models
65C99 Probabilistic methods, stochastic differential equations
62G05 Nonparametric estimation
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