Mixture models may be a useful and flexible tool to describe data with a complicated structure, for instance characterized by multimodality or asymmetry. In a Bayesian setting, it is a well established fact that one need to be careful in using improper prior distributions, since the posterior...

Importance sampling methods can be iterated like MCMC algorithms, while being more robust against dependence and starting values. The population Monte Carlo principle consists of iterated generations of importance samples, with importance functions depending on the previously generated...

Our aim is to analyze the link between optimism and risk aversion in a subjective expected utility setting and to estimate the average level of optimism when weighted by risk tolerance. Its estimation leads to a non-trivial statistical problem. We start from a large lottery survey (1536...

This note is an extended review of the book Error and Inference, edited by Deborah Mayo and Aris Spanos, about their frequentist and philosophical perspective on testing of hypothesis and on the criticisms of alternatives like the Bayesian approach.

Missing variable models are typical benchmarks for new computational techniques in that the ill-posed nature of missing variable models offer a challenging testing ground for these techniques. This was the case for the EM algorithm and the Gibbs sampler, and this is also true for importance...

For numerous models, it is impossible to conduct an exact Bayesian inference. There are many cases where the derivation of the posterior distribution leads to intractable calculations (due to the fact that this generally involves intractable integrations). The Bayesian computational literature...

This thesis presents contributions to the Monte Carlo methodology used in Bayesian statistics. The Bayesian framework is one of the main approaches to statistics and includes a rich methodology to perform inference and model choice. However, as statistical models become more realistic and drift...

The missionary zeal of many Bayesians of old has been matched, in the other direction, by an attitude among some theoreticians that Bayesian methods were absurd—not merely misguided but obviously wrong in principle. We consider several examples, beginning with Feller's classic text on...

In 1996, Propp and Wilson introduced coupling from the past (CFTP), an algorithm for generating a sample from the exact stationary distribution of a Markov chain. In 1998, Fill proposed another so–called perfect sampling algorithm. These algorithms have enormous potential in Markov Chain Monte...