165 citations · 521 across the 31 of their papers we have counts for
8 papers · 1 filter
Sequential sampling of junction trees for decomposable graphs
Jimmy Olsson, Tetyana Pavlenko, Felix L. Rios
The junction-tree representation provides an attractive structural property for organizing a decomposable graph. In this study, we present two novel stochastic algorithms, which we…
Bayesian learning of weakly structural Markov graph laws using sequential Monte Carlo methods
Jimmy Olsson, Tetyana Pavlenko, Felix L. Rios
We present a sequential sampling methodology for weakly structural Markov laws, arising naturally in a Bayesian structure learning context for decomposable graphical models. As a k…
Posterior consistency for partially observed Markov models
Randal Douc, Jimmy Olsson, Francois Roueff
In this work we establish the posterior consistency for a parametrized family of partially observed, fully dominated Markov models. As a main assumption, we suppose that the prior…
Long-term stability of sequential Monte Carlo methods under verifiable conditions
Randal Douc, Eric Moulines, Jimmy Olsson
This paper discusses particle filtering in general hidden Markov models (HMMs) and presents novel theoretical results on the long-term stability of bootstrap-type particle filters.…
Particle-based likelihood inference in partially observed diffusion processes using generalised Poisson estimators
Jimmy Olsson, Jonas Ströjby
This paper concerns the use of the expectation-maximisation (EM) algorithm for inference in partially observed diffusion processes. In this context, a well known problem is that al…
Consistency of the maximum likelihood estimator for general hidden Markov models
Randal Douc, Eric Moulines, Jimmy Olsson +1
Consider a parametrized family of general hidden Markov models, where both the observed and unobserved components take values in a complete separable metric space. We prove that th…
On the auxiliary particle filter
Randal Douc, Eric Moulines, Jimmy Olsson
In this article we study asymptotic properties of weighted samples produced by the auxiliary particle filter (APF) proposed by pitt and shephard (1999). Besides establishing a cent…
Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models
Jimmy Olsson, Olivier Cappé, Randal Douc +1
This paper concerns the use of sequential Monte Carlo methods (SMC) for smoothing in general state space models. A well-known problem when applying the standard SMC technique in th…