32 citations · 182 across the 41 of their papers we have counts for
15 papers · 1 filter
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…
Online Rolling Controlled Sequential Monte Carlo
Liwen Xue, Axel Finke, Adam M. Johansen
We introduce methodology for real-time inference in general-state-space hidden Markov models. Specifically, we extend recent advances in controlled sequential Monte Carlo (CSMC) me…
Inference for Diffusion Processes via Controlled Sequential Monte Carlo and Splitting Schemes
Shu Huang, Richard G. Everitt, Massimiliano Tamborrino +1
We introduce an inferential framework for a wide class of semi-linear stochastic differential equations (SDEs). Recent work has shown that numerical splitting schemes can preserve…
Solving Fredholm Integral Equations of the Second Kind via Wasserstein Gradient Flows
Francesca R. Crucinio, Adam M. Johansen
Motivated by a recent method for approximate solution of Fredholm equations of the first kind, we develop a corresponding method for a class of Fredholm equations of the \emph{seco…
Properties of Marginal Sequential Monte Carlo Methods
Francesca R. Crucinio, Adam M. Johansen
We provide a framework which admits a number of ``marginal'' sequential Monte Carlo (SMC) algorithms as particular cases -- including the marginal particle filter [Klaas et al., 20…
Particle algorithms for maximum likelihood training of latent variable models
Juan Kuntz, Jen Ning Lim, Adam M. Johansen
(Neal and Hinton, 1998) recast maximum likelihood estimation of any given latent variable model as the minimization of a free energy functional , and the EM algorithm as coordin…