26 citations · 52 across the 6 of their papers we have counts for
8 papers
Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models
Sara Pérez-Vieites, Joaquín Míguez
We introduce a new sequential methodology to calibrate the fixed parameters and track the stochastic dynamical variables of a state-space system. The proposed method is based on th…
On the performance of particle filters with adaptive number of particles
Víctor Elvira, Joaquín Míguez, Petar M. Djurić
We investigate the performance of a class of particle filters (PFs) that can automatically tune their computational complexity by evaluating online certain predictive statistics wh…
A probabilistic scheme for joint parameter estimation and state prediction in complex dynamical systems
Sara Pérez-Vieites, Inés P. Mariño, Joaquín Míguez
Many problems in the geophysical sciences demand the ability to calibrate the parameters and predict the time evolution of complex dynamical models using sequentially-collected dat…
Importance sampling with transformed weights
Manuel A. Vázquez, Joaquín Míguez
The importance sampling (IS) method lies at the core of many Monte Carlo-based techniques. IS allows the approximation of a target probability distribution by drawing samples from…
A comparison of nonlinear population Monte Carlo and particle Markov chain Monte Carlo algorithms for Bayesian inference in stochastic kinetic models
Eugenia Koblents, Joaquín Míguez
In this paper we address the problem of Monte Carlo approximation of posterior probability distributions in stochastic kinetic models (SKMs). SKMs are multivariate Markov jump proc…
On the Generalized Ratio of Uniforms as a Combination of Transformed Rejection and Extended Inverse of Density Sampling
Luca Martino, David Luengo, Joaquín Míguez
In this work we investigate the relationship among three classical sampling techniques: the inverse of density (Khintchine's theorem), the transformed rejection (TR) and the genera…
Two adaptive rejection sampling schemes for probability density functions log-convex tails
Luca Martino, Joaquín Míguez
Monte Carlo methods are often necessary for the implementation of optimal Bayesian estimators. A fundamental technique that can be used to generate samples from virtually any targe…
Particle-kernel estimation of the filter density in state-space models
Dan Crisan, Joaquín Míguez
Sequential Monte Carlo (SMC) methods, also known as particle filters, are simulation-based recursive algorithms for the approximation of the a posteriori probability measures gener…