A hybrid Markov chain for the Bayesian analysis of the multinomial probit model |
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Authors: | Agostino Nobile |
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Institution: | (1) Department of Mathematics, University of Bristol, Bristol, BS8 1TW, UK |
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Abstract: | Bayesian inference for the multinomial probit model, using the Gibbs sampler with data augmentation, has been recently considered by some authors. The present paper introduces a modification of the sampling technique, by defining a hybrid Markov chain in which, after each Gibbs sampling cycle, a Metropolis step is carried out along a direction of constant likelihood. Examples with simulated data sets motivate and illustrate the new technique. A proof of the ergodicity of the hybrid Markov chain is also given. |
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Keywords: | Multinomial probit model Gibbs sampling Metropolis algorithm Bayesian analysis |
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