29 citations · 73 across the 15 of their papers we have counts for
5 papers · 1 filter
Theoretical guarantees for lifted samplers
Philippe Gagnon, Florian Maire
Lifted samplers form a class of Markov chain Monte Carlo methods which has drawn a lot attention in recent years due to superior performance in challenging Bayesian applications. A…
Improving multiple-try Metropolis with local balancing
Philippe Gagnon, Florian Maire, Giacomo Zanella
Multiple-try Metropolis (MTM) is a popular Markov chain Monte Carlo method with the appealing feature of being amenable to parallel computing. At each iteration, it samples several…
An asymptotic Peskun ordering and its application to lifted samplers
Philippe Gagnon, Florian Maire
A Peskun ordering between two samplers, implying a dominance of one over the other, is known among the Markov chain Monte Carlo community for being a remarkably strong result. It i…
Informed reversible jump algorithms
Philippe Gagnon
Incorporating information about the target distribution in proposal mechanisms generally produces efficient Markov chain Monte Carlo algorithms (or at least, algorithms that are mo…
Non-reversible jump algorithms for Bayesian nested model selection
Philippe Gagnon, Arnaud Doucet
Non-reversible Markov chain Monte Carlo methods often outperform their reversible counterparts in terms of asymptotic variance of ergodic averages and mixing properties. Lifting th…