Three new papers from the group “in press”: 

The paper “A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models” by E. Koblents and J. Miguez; to appear in Statistics and Computing. (PDF)  
This paper addresses the Monte Carlo approximation of posterior probability distributions. In particular, we consider the population Monte Carlo (PMC) technique, which is based on an iterative importance sampling (IS) approach. An important drawback of this methodology is the degeneracy of the importanceweights (IWs) when the dimension of either the observations or the variables of interest is high. To alleviate this difficulty, we propose a new method thatperforms a nonlinear transformation of the IWs. This operation reduces the weight variation, hence it avoids degeneracy and increases the efficiency of the IS scheme, specially when drawing from proposal functions which are poorly adapted to the true posterior. For the sake of illustration, we have applied the proposed algorithm to the estimation of the parameters of a Gaussian mixture model. This is a simple problem that enables us to discuss the main features of the proposed technique. As a practical application, we have also considered the challenging problem of estimating the rate parameters of a stochastic kinetic model (SKM). SKMs are multivariate systems that model molecular interactions in biological and chemical problems. We introduce a particularization of the proposed algorithm to SKMs and present numerical results.  
The paper “A RaoBlackwellized particle filter for joint parameter estimation and biomass tracking in a stochastic predatorprey system” by L. MartinFernandez, G. Gilioli, E. Lanzarone, J. Miguez, S. Pasquali, F. Ruggeri and D. P. Ruiz; to appear in Mathematical Biosciences and Engineering. (PDF)  
Functional response estimation and population tracking in predatorprey systems are critical problems in ecology. In this paper we consider a stochastic predatorprey system with a LotkaVolterra functional response and propose a particle filtering method for: (a) estimating the behavioral parameter representing the rate of effective search per predator in the functional response and (b) forecasting the population biomass using field data. In particular, the proposed technique combines a sequential Monte Carlo sampling scheme for tracking the timevarying biomass with the analytical integration of the unknown behavioral parameter. In order to assess the performance of the method, we show results for both synthetic and observed data collected in an acarine predatorprey system, namely the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis.  
The paper “Particlekernel estimation of the filter density in statespace models” by D. Crisan, J. Miguez; to appear in Bernoulli. (PDF)  
Sequential Monte Carlo (SMC) methods, also known as particle filters, are simulationbased recursive algorithms for the approximation of the a posteriori probability measures generated by statespace dynamical models. At any given time t, a SMC method produces a set of samples over the state space of the system of interest (often termed “particles”) that is used to build a discrete and random approximation of the posterior probability distribution of the state variables, conditional on a sequence of available observations. One potential application of the methodology is the estimation of the densities associated to the sequence of a posteriori distributions. While practitioners have rather freely applied such density approximations in the past, the issue has received less attention from a theoretical perspective. In this paper, we address the problem of constructing kernelbased estimates of the posterior probability density function and its derivatives, and obtain asymptotic convergence results for the estimation errors. In particular, we find convergence rates for the approximation errors that hold uniformly on the state space and guarantee that the error vanishes almost surely as the number of particles in the filter grows. Based on this uniform convergence result, we first show how to build continuous measures that converge almost surely (with known rate) toward the posterior measure and then address a few applications. The latter include maximum a posteriori estimation of the system state using the approximate derivatives of the posterior density and the approximation of functionals of it, e.g., Shannon’s entropy. 