45 citations · 292 across the 21 of their papers we have counts for
22 papers
Resampling in conditional SMC algorithms
Axel Finke, Adam M. Johansen, Anthony Lee +1
Conditional sequential Monte Carlo (CSMC) algorithms arise in particle Markov chain Monte Carlo and a number of related settings. As in standard sequential Monte Carlo (SMC) algori…
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…
Lazy object copy as a platform for population-based probabilistic programming
Lawrence M. Murray
This work considers dynamic memory management for population-based probabilistic programs, such as those using particle methods for inference. Such programs exhibit a pattern of al…
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…
Particle filter with rejection control and unbiased estimator of the marginal likelihood
Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön +1
We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties o…
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…