Optimal trans-model MCMC mixing requires proposing high-posterior models and posterior-matched parameters; maximum jump probability is necessary but not always sufficient for maximum efficiency.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.CO 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Mixing efficiency of trans-model Markov chain Monte Carlo algorithms with applications in Bayesian phylogenetics
Optimal trans-model MCMC mixing requires proposing high-posterior models and posterior-matched parameters; maximum jump probability is necessary but not always sufficient for maximum efficiency.