5 papers · 1 filter
Automatic tempered posterior distributions for Bayesian inversion problems
L. Martino, F. Llorente, E. Curbelo +2
We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. Mo…
Convergence rates for optimised adaptive importance samplers
Ömer Deniz Akyildiz, Joaquín Míguez
Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which \textit{adapt} themselves to obtain better…
The Incremental Proximal Method: A Probabilistic Perspective
Ömer Deniz Akyildiz, Victor Elvira, Joaquin Miguez
In this work, we highlight a connection between the incremental proximal method and stochastic filters. We begin by showing that the proximal operators coincide, and hence can be r…
Analysis of a nonlinear importance sampling scheme for Bayesian parameter estimation in state-space models
Joaquin Miguez, Ines P. Mariño, Manuel A. Vazquez
The Bayesian estimation of the unknown parameters of state-space (dynamical) systems has received considerable attention over the past decade, with a handful of powerful algorithms…
Uniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state--space Markov models
Dan Crisan, Joaquin Miguez
We analyse the performance of a recursive Monte Carlo method for the Bayesian estimation of the static parameters of a discrete--time state--space Markov model. The algorithm emplo…