Abstract
We consider a class of adaptive MCMC algorithms using a Langevin-type proposal density. We state and prove regularity conditions for the convergence of these algorithms. In addition to these theoretical results we introduce a number of methodological innovations that can be applied much more generally. We assess the performance of these algorithms with simulation studies, including an example of the statistical analysis of a point process driven by a latent log-Gaussian Cox process.
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Marshall, T., Roberts, G. An adaptive approach to Langevin MCMC. Stat Comput 22, 1041–1057 (2012). https://doi.org/10.1007/s11222-011-9276-6
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DOI: https://doi.org/10.1007/s11222-011-9276-6