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Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics
High-dimensional limit theorems have been shown useful to derive tuning rules for finding the optimal scaling in random walk Metropolis algorithms. The assumptions under which weak convergence results are proved are, however, restrictive: the target density is typically assumed to be of a product fo...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer US
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924149/ https://www.ncbi.nlm.nih.gov/pubmed/35310543 http://dx.doi.org/10.1007/s11222-022-10080-8 |