On computational tools for Bayesian data analysis

While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the current chapter details its practical aspects through a review of the computational methods available for approximating Bayesian procedures. Recent innovations like Monte Carlo Markov chain, sequential Monte Carlo methods and more recently Approximate Bayesian Computation techniques have considerably increased the potential for Bayesian applications and they have also opened new avenues for Bayesian inference, first and foremost Bayesian model choice.Comment: This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 pages, 9 figure

Importance sampling methods for Bayesian discrimination between embedded models

This paper surveys some well-established approaches on the approximation of Bayes factors used in Bayesian model choice, mostly as covered in Chen et al. (2000). Our focus here is on methods that are based on importance sampling strategies rather than variable dimension techniques like reversible jump MCMC, including: crude Monte Carlo, maximum likelihood based importance sampling, bridge and harmonic mean sampling, as well as Chib's method based on the exploitation of a functional equality. We demonstrate in this survey how these different methods can be efficiently implemented for testing the significance of a predictive variable in a probit model. Finally, we compare their performances on a real dataset

Bayesian Core: The Complete Solution Manual

This solution manual contains the unabridged and original solutions to all the exercises proposed in Bayesian Core, along with R programs when necessary.Comment: 118+vii pages, 21 figures, 152 solution

Efficient learning in ABC algorithms

Approximate Bayesian Computation has been successfully used in population genetics to bypass the calculation of the likelihood. These methods provide accurate estimates of the posterior distribution by comparing the observed dataset to a sample of datasets simulated from the model. Although parallelization is easily achieved, computation times for ensuring a suitable approximation quality of the posterior distribution are still high. To alleviate the computational burden, we propose an adaptive, sequential algorithm that runs faster than other ABC algorithms but maintains accuracy of the approximation. This proposal relies on the sequential Monte Carlo sampler of Del Moral et al. (2012) but is calibrated to reduce the number of simulations from the model. The paper concludes with numerical experiments on a toy example and on a population genetic study of Apis mellifera, where our algorithm was shown to be faster than traditional ABC schemes

The Diversity of Design of TSOs

International audienceIt is puzzling today to explain diversity and imperfection of actual transmission monopoly designs in competitive electricity markets. We argue that transmission monopoly in competitive electricity markets has to be analysed within a Wilson (2002) modular framework. Applied to the management of electricity flows, at least three modules make the core of transmission design: 1° the short run management of network externalities; 2° the long run management of network investment; and 3° the coordination of neighboring Transmission System Operators for cross border trade. In order to tackle this diversity of designs of TSOs, we show that for each of these modules, three different basic ways of managing them are possible. Among the identified twenty seven options of organisation, we define an Ideal TSO. Second, we demonstrate that 1°monopoly design differs from this Ideal TSO and cannot handle these three modules irrespective of the “institutional” definition and allocation of property rights on transmission; while 2°definition and allocation of property rights on transmission cannot ignore the existing electrical industry and transmission network structure: they have to complement each other to be efficient. Some conclusions for regulatory issues of transmission systems operators are derived from this analysis of network monopoly organisation

Bayesian Modelling and Inference on Mixtures of Distributions.

bayesian models;