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...

En estimation bayésienne, lorsque le calcul explicite de la loi a posteriori du vecteur des paramètres à estimer est impossible, les méthodes de Monte-Carlo par chaînes de Markov (MCMC) [Robert and Casella, 1999] permettent théoriquement de fournir un échantillon approximativement...

This document is the aggregation of several discussions of Lopes et al. (2010) we submitted tothe proceedings of the Ninth Valencia Meeting, held in Benidorm, Spain, on June 3–8, 2010, inconjunction with Hedibert Lopes’ talk at this meeting. The main point in those discussions is...

This chapter provides a overview of Bayesian inference, mostly emphasising that it is auniversal method for summarising uncertainty and making estimates and predictions usingprobability statements conditional on observed data and an assumed model (Gelman 2008).The Bayesian perspective is thus...

type="main" xml:id="rssb12056-abs-0001" <title type="main">Summary</title> <p>The choice of the summary statistics that are used in Bayesian inference and in particular in approximate Bayesian computation algorithms has bearings on the validation of the resulting inference. Those statistics are nonetheless customarily used...</p>

The objective of this paper is to adopt a general equilibrium model and determine the socially efficient discount factor and discount rate when there are heterogeneous anticipations about the future of the economy as well as heterogeneous time preference rates. Among others we tackle the...

Casella and Robert (1996) presented a general Rao--Blackwellisation principle for accept-reject and Metropolis-Hastings schemes that leads to significant decreases in the variance of the resulting estimators, but at a high cost in computing and storage. Adopting a completely different...

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...