Adaptive Metropolis-Hastings samplers for the Bayesian analysis of large linear Gaussian systems
نویسندگان
چکیده
This paper considers the implementation of efficient Bayesian computation for large linear Gaussian models containing many latent variables. A common approach is to implement a simple MCMC procedure such as the Gibbs sampler or data augmentation, but these methods are often unsatisfactory when the model is large. This motivates the need to develop other strategies for improving MCMC. This paper considers the combination of adapting algorithms with the Metropolis-Hastings scheme in the construction of efficient MCMC schemes with good mixing properties.
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