Transport Reversible Jump Proposals

1 code implementation • 22 Oct 2022 • Laurence Davies, Robert Salomone, Matthew Sutton, Christopher Drovandi

Reversible jump Markov chain Monte Carlo (RJMCMC) proposals that achieve reasonable acceptance rates and mixing are notoriously difficult to design in most applications.

Density Estimation

Continuously-Tempered PDMP Samplers

no code implementations • 19 May 2022 • Matthew Sutton, Robert Salomone, Augustin Chevallier, Paul Fearnhead

We show how PDMPs, and particularly the Zig-Zag sampler, can be implemented to sample from such an extended distribution.

Reversible Jump PDMP Samplers for Variable Selection

1 code implementation • 22 Oct 2020 • Augustin Chevallier, Paul Fearnhead, Matthew Sutton

A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-reversible, can mix better than standard MCMC algorithms, and can use subsampling ideas to speed up computation in big data scenarios.

Variable Selection

A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data

1 code implementation • 23 Feb 2017 • Pierre Lafaye de Micheaux, Benoit Liquet, Matthew Sutton

Partial Least Squares (PLS) methods have been heavily exploited to analyse the association between two blocs of data.

Mathematical Proofs