3 papers
stat.CO2026
Laplace and skew-Laplace approximations for Dirichlet process mixture posterior density
Beatrice Franzolini, Francesco Pozza
Posterior inference for Dirichlet process mixture models is analytically intractable and typically relies on Markov chain Monte Carlo methods, which can become computationally proh…
stat.CO2026
Zeroth-order parallel sampling
Francesco Pozza, Giacomo Zanella
Finding effective ways to exploit parallel computing to accelerate Markov chain Monte Carlo methods is an important problem in Bayesian computation and related disciplines. In this…
stat.CO2024
On the fundamental limitations of multiproposal Markov chain Monte Carlo algorithms
Francesco Pozza, Giacomo Zanella
We study multiproposal Markov chain Monte Carlo algorithms, such as Multiple-try or generalised Metropolis-Hastings schemes, which have recently received renewed attention due to t…