Gibbs samplers are popular algorithms to approximate posterior distribut...
Multiple-try Metropolis (MTM) is a popular Markov chain Monte Carlo meth...
Leave-one-out cross-validation (LOO-CV) is a popular method for estimati...
A broad class of models that routinely appear in several fields can be
e...
Dirichlet process mixtures are flexible non-parametric models, particula...
We study the class of first-order locally-balanced Metropolis–Hastings
a...
The article is about algorithms for learning Bayesian hierarchical model...
We study a recently introduced gradient-based Markov chain Monte Carlo m...
Traditional Bayesian random partition models assume that the size of eac...
State-of-the-art methods for Bayesian inference on regression models wit...
State-of-the-art methods for Bayesian inference on regression models wit...
We analyse the tension between robustness and efficiency for Markov chai...
We propose a Monte Carlo algorithm to sample from high-dimensional
proba...
We analyze the complexity of Gibbs samplers for inference in crossed ran...
There is a lack of methodological results to design efficient Markov cha...
We consider the problem of approximating the product of n expectations w...
Most generative models for clustering implicitly assume that the number ...