Unconstrained Metropolis–Hastings Sampling of Covariance Matrices
Markov chain Monte Carlo (MCMC), the predominant algorithm for fitting hierarchal models to data in a Bayesian setting, relies on the ability to sample from the full conditional distributions of unobserved parameters. Covariance or precision matrices offer a unique sampling challenge due to the cons...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/jpas/4744162 |
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