123 citations · 876 across the 50 of their papers we have counts for
8 papers · 1 filter
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
Johan Dahlin, Fredrik Lindsten, Joel Kronander +1
Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Bayesian inference in models where the posterior distribution is analytical intractable or computationally costl…
Blocking Strategies and Stability of Particle Gibbs Samplers
Sumeetpal S. Singh, Fredrik Lindsten, Eric Moulines
Sampling from the conditional (or posterior) probability distribution of the latent states of a Hidden Markov Model, given the realization of the observed process, is a non-trivial…
Rao-Blackwellized particle smoothers for conditionally linear Gaussian models
Fredrik Lindsten, Pete Bunch, Simo Särkkä +2
Sequential Monte Carlo (SMC) methods, such as the particle filter, are by now one of the standard computational techniques for addressing the filtering problem in general state-spa…
Particle ancestor sampling for near-degenerate or intractable state transition models
Fredrik Lindsten, Pete Bunch, Sumeetpal S. Singh +1
We consider Bayesian inference in sequential latent variable models in general, and in nonlinear state space models in particular (i.e., state smoothing). We work with sequential M…
Sequential Monte Carlo Methods for System Identification
Thomas B. Schön, Fredrik Lindsten, Johan Dahlin +4
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo…
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
Simon Lacoste-Julien, Fredrik Lindsten, Francis Bach
Recently, the Frank-Wolfe optimization algorithm was suggested as a procedure to obtain adaptive quadrature rules for integrals of functions in a reproducing kernel Hilbert space (…