47 citations · 219 across the 37 of their papers we have counts for
3 papers · 1 filter
High-dimensional Filtering using Nested Sequential Monte Carlo
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without good proposal distributions struggle in…
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman +1
Variational inference using the reparameterization trick has enabled large-scale approximate Bayesian inference in complex probabilistic models, leveraging stochastic optimization…
Interacting Particle Markov Chain Monte Carlo
Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten +4
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like r…