Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational effort to yield reliable inferences. One of the major challenges in phylogenetics regards the estimation of the marginal likelihood. This quantity is commonly used for comparing different evolutionary models, but its calculation, eve...

We investigate properties of Bayesian networks (BNs) in the context of robust state estimation. We focus on problems where state estimation can be viewed as a classification of the possible states, which in turn is based on the fusion of heterogeneous and noisy information. We introduce a coarse perspective of the inference processes and show that classification with BNs can be very robust, eve...

This work discuses a novel algorithm for joint sparse estimation of superimposed signals and their parameters. The proposed method is based on two concepts: a variational Bayesian version of the incremental sparse Bayesian learning (SBL)– fast variational SBL – and a variational Bayesian approach for parameter estimation of superimposed signal models. Both schemes estimate the unknown parameter...

We study parameter inference in large-scale latent variable models. We first propose an unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed frequentist or Bayesian methods. We then propose a novel inference method for the frequentist estimation of parameters, that adapts MCMC method...

With the rising amount of available multilingual text data, computational linguistics faces an opportunity and a challenge. This text can enrich the domains of NLP applications and improve their performance. Traditional supervised learning for this kind of data would require annotation of part of this text for induction of natural language structure. For these large amounts of rich text, such a...

FAmle is a R package that may be used in order to carry out tasks that pertain to univariate frequency analysis. Basically, two general frameworks are being entertained here: maximum likelihood and Bayesian. More precisely, for a given problem, the user may decide to estimate the unknown parameters via maximum likelihood, and to then proceed to statistical inference accordingly, or to use a ful...

Common nonparametric curve tting methods such as spline smooth ing local polynomial regression and basis function approaches are now well devel oped and widely applied More recently Bayesian function estimation has become a useful supplementary or alternative tool for practical data analysis mainly due to breakthroughs in computerintensive inference via Markov chain Monte Carlo simulation This ...

Factor analysis is a standard method for multivariate analysis. The sampling model in the most popular factor analysis is Gaussian and has thus often been criticized for its lack of robustness. A simple robust extension of the Gaussian factor analysis model is obtained by replacing the multivariate Gaussian distribution with a multivariate t-distribution. We develop computational methods for bo...

We consider geostatistical models that allow the locations at which data are collected to be informative about the outcomes. Diggle et al. [2009] refer to this problem as preferential sampling, though we use the term informative sampling to highlight the relationship with the longitudinal data literature on informative observation times. In the longitudinal setting, joint models of the observat...

We consider geostatistical models that allow the locations at which data are collected to be informative about the outcomes. A Bayesian approach is proposed, which models the locations using a log Gaussian Cox process, while modelling the outcomes conditionally on the locations as Gaussian with a Gaussian process spatial random effect and adjustment for the location intensity process. We prove ...