Monte Carlo methods are becoming more and more popular in statistics due to the fast development of efficient computing technologies. One of the major beneficiaries of this advent is the field of Bayesian inference. The aim of this thesis is two-fold: (i) to explain the theory justifying the validity of the simulation-based schemes in a Bayesian setting (why they should work) and (ii) to apply them in several different types of data analysis that a statistician has to routinely encounter. In Chapter 1, I introduce key concepts in Bayesian statistics. Then we discuss Monte Carlo Simulation methods in detail. Our particular focus in on, Markov Chain Monte Carlo, one of the most important tools in Bayesian inference. We discussed three differe...
ISBN:978-2-7598-1032-1International audienceBayesian inference often requires integrating some funct...
This thesis addresses several issues appearing in Bayesian statistics. Firstly, computations for app...
La modélisation probabiliste et l'inférence bayésienne computationnelles rencontrent un très grand s...
Bayesian methods provide the means for studying probabilistic models of linear as well as non-linear...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
I consider the development of Markov chain Monte Carlo (MCMC) methods, from late-1980s Gibbs samplin...
The Markov Chain Monte-Carlo (MCMC) born in early 1950s has recently aroused great interest among s...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
These notes provide an introduction to Markov chain Monte Carlo methods that are useful in both Baye...
This chapter reviews the recent developments in Markov chain Monte Carlo simulation methods. These m...
In this paper we discuss several aspects of simulation based Bayesian econometric inference. We star...
Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account fo...
Bayesian inference is an important branch in statistical sciences. The subject of this thesis is abo...
International audienceThis chapter surveys the most standard Monte Carlo methods available for simul...
We present several Markov chain Monte Carlo simulation methods that have been widely used in recent ...
ISBN:978-2-7598-1032-1International audienceBayesian inference often requires integrating some funct...
This thesis addresses several issues appearing in Bayesian statistics. Firstly, computations for app...
La modélisation probabiliste et l'inférence bayésienne computationnelles rencontrent un très grand s...
Bayesian methods provide the means for studying probabilistic models of linear as well as non-linear...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
I consider the development of Markov chain Monte Carlo (MCMC) methods, from late-1980s Gibbs samplin...
The Markov Chain Monte-Carlo (MCMC) born in early 1950s has recently aroused great interest among s...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
These notes provide an introduction to Markov chain Monte Carlo methods that are useful in both Baye...
This chapter reviews the recent developments in Markov chain Monte Carlo simulation methods. These m...
In this paper we discuss several aspects of simulation based Bayesian econometric inference. We star...
Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account fo...
Bayesian inference is an important branch in statistical sciences. The subject of this thesis is abo...
International audienceThis chapter surveys the most standard Monte Carlo methods available for simul...
We present several Markov chain Monte Carlo simulation methods that have been widely used in recent ...
ISBN:978-2-7598-1032-1International audienceBayesian inference often requires integrating some funct...
This thesis addresses several issues appearing in Bayesian statistics. Firstly, computations for app...
La modélisation probabiliste et l'inférence bayésienne computationnelles rencontrent un très grand s...