We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to both noninteracting PMCMC samplers and a s...

Phylogenetic inference is an intractable statistical problem on a complex space. Markov chain Monte Carlo methods are the primary tool for Bayesian phylogenetic inference, but it challenging to construct efficient schemes explore associated posterior distribution or assess their performance. Existing approaches unable diagnose mixing convergence of jointly across all components model. Lagged co...

Proximal Markov chain Monte Carlo is a novel construct that lies at the intersection of Bayesian computation and convex optimization, which helped popularize use nondifferentiable priors in statistics. Existing formulations proximal MCMC, however, require hyperparameters regularization parameters to be prespecified. In this article, we extend paradigm MCMC through introducing new class called e...