Abstract
In this paper, we improve upon the Carlin and Chib Markov chain Monte Carlo algorithm that searches in model and parameter spaces. Our proposed algorithm attempts non-uniformly chosen ālocalā moves in the model space and avoids some pitfalls of other existing algorithms. In a series of examples with linear and logistic regression, we report evidence that our proposed algorithm performs better than the existing algorithms.
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