69 citations · 281 across the 19 of their papers we have counts for
23 papers
Nested smoothing algorithms for inference and tracking of heterogeneous multi-scale state-space systems
Sara Pérez-Vieites, Harold Molina-Bulla, Joaquin Miguez
Multi-scale problems, where variables of interest evolve in different time-scales and live in different state-spaces, can be found in many fields of science. Here, we introduce a n…
Sequential discretisation schemes for a class of stochastic differential equations and their application to Bayesian filtering
Deniz Akyildiz, Dan Crisan, Joaquin Miguez
We introduce a predictor-corrector discretisation scheme for the numerical integration of a class of stochastic differential equations and prove that it converges with weak order 1…
A Bayesian inference and model selection algorithm with an optimisation scheme to infer the model noise power
J. Lopez-Santiago, L. Martino, J. Miguez +1
Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate…
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…
Polynomial propagation of moments in stochastic differential equations
Albert López-Yela, Joaquin Miguez
We address the problem of approximating the moments of the solution, , of an Itô stochastic differential equation (SDE) with drift and a diffusion terms over a t…
A likely magnetic activity cycle for the exoplanet host M dwarf GJ 3512
J. Lopez-Santiago, L. Martino, J. Miguez +1
Current radial velocity data from specialized instruments contain a large amount of information that may pass unnoticed if their analysis is not accurate. The joint use of Bayesian…
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…
A probabilistic incremental proximal gradient method
Ömer Deniz Akyildiz, Émilie Chouzenoux, Víctor Elvira +1
In this paper, we propose a probabilistic optimization method, named probabilistic incremental proximal gradient (PIPG) method, by developing a probabilistic interpretation of the…
Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization
Ömer Deniz Akyildiz, Dan Crisan, Joaquín Míguez
We introduce and analyze a parallel sequential Monte Carlo methodology for the numerical solution of optimization problems that involve the minimization of a cost function that con…
Stable approximation schemes for optimal filters
Dan Crisan, Alberto Lopez-Yela, Joaquin Miguez
A stable filter has the property that it asymptotically `forgets' initial perturbations. As a result of this property, it is possible to construct approximations of such filters wh…
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…
Nudging the particle filter
Ömer Deniz Akyıldız, Joaquín Míguez
We investigate a new sampling scheme aimed at improving the performance of particle filters whenever (a) there is a significant mismatch between the assumed model dynamics and the…
Multiplex decomposition of non-Markovian dynamics and the hidden layer reconstruction problem
Lucas Lacasa, Inés P. Mariño, Joaquín Miguez +5
Elements composing complex systems usually interact in several different ways and as such the interaction architecture is well modelled by a multiplex network. However often this a…
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…
An iterative importance sampler for Bayesian parameter estimation in stochastic models of multicellular clocks
Inés P. Mariño, Joaquin Miguez, Alexey Zaikin
We investigate a stochastic version of the synthetic multicellular clock model proposed by Garcia-Ojalvo, Elowitz and Strogatz. By introducing dynamical noise in the model and assu…
A nonlinear population Monte Carlo scheme for the Bayesian estimation of parameters of -stable distributions
Eugenia Koblents, Joaquin Miguez, Marco A. Rodriguez +1
The class of -stable distributions enjoys multiple practical applications in signal processing, finance, biology and other areas because it allows to describe interesting and co…
Adapting the Number of Particles in Sequential Monte Carlo Methods through an Online Scheme for Convergence Assessment
Víctor Elvira, Joaquín Míguez, Petar M. Djurić
Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means of sets of weighted particles. While the convergence of the…
A proof of uniform convergence over time for a distributed particle filter
Joaquin Miguez, Manuel A. Vazquez
Distributed signal processing algorithms have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters (PFs). Ho…
A simple scheme for the parallelization of particle filters and its application to the tracking of complex stochastic systems
Dan Crisan, Joaquin Miguez, Gonzalo Rios
We investigate the use of possibly the simplest scheme for the parallelisation of the standard particle filter, that consists in splitting the computational budget into fully i…
Nested particle filters for online parameter estimation in discrete-time state-space Markov models
Dan Crisan, Joaquin Miguez
We address the problem of approximating the posterior probability distribution of the fixed parameters of a state-space dynamical system using a sequential Monte Carlo method. The…
A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models
Eugenia Koblents, Joaquín Míguez
This paper addresses the problem of Monte Carlo approximation of posterior probability distributions. In particular, we have considered a recently proposed technique known as popul…
Generalized Rejection Sampling Schemes and Applications in Signal Processing
Luca Martino, Joaquin Miguez
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become very popular…