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4 papers · 1 filter
Anytime Parallel Tempering
A. Marie d'Avigneau, S. S. Singh, L. M. Murray
Developing efficient MCMC algorithms is indispensable in Bayesian inference. In parallel tempering, multiple interacting MCMC chains run to more efficiently explore the state space…
Parameter elimination in particle Gibbs sampling
Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray +1
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories. A viable approach is particle Markov chain Monte Carlo, combining MCMC and seque…
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist +1
We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle fi…
Anytime Monte Carlo
Lawrence M. Murray, Sumeetpal Singh, Anthony Lee
Monte Carlo algorithms simulate some prescribed number of samples, taking some random real time to complete the computations necessary. This work considers the converse: to impose…