This is a revised version of a chapter written for the Handbook of Computational Statistics, edited by J. Gentle, W. Hardle and Y. Mori in 2003, in preparation for the second editionIn this chapter, we will first present the most standard computational challenges met in Bayesian Statistics, focussing primarily on mixture estimation and on model choice issues, and then relate these problems with computational solutions. Of course, this chapter is only a terse introduction to the problems and solutions related to Bayesian computations. For more complete references, see Robert and Casella (2004, 2009), or Marin and Robert (2007), among others. We also restrain from providing an introduction to Bayesian Statistics per se and for comprehensive c...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
Abstract: This chapter surveys advances in the field of Bayesian com-putation over the past twenty y...
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most i...
This is a revised version of a chapter written for the Handbook of Computational Statistics, edited ...
In this chapter, we will first present the most standard computational challenges met in Bayesian St...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
If, in the mid 1980's, one had asked the average statistician about the di-culties of using Bayesian...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
The emergence in the past years of Bayesian analysis in many methodological and applied fields as th...
The emergence in the past years of Bayesian analysis in many methodological and applied fields as th...
International audienceThis chapter surveys the most standard Monte Carlo methods available for simul...
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
Abstract: This chapter surveys advances in the field of Bayesian com-putation over the past twenty y...
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most i...
This is a revised version of a chapter written for the Handbook of Computational Statistics, edited ...
In this chapter, we will first present the most standard computational challenges met in Bayesian St...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 ...
If, in the mid 1980's, one had asked the average statistician about the di-culties of using Bayesian...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
The emergence in the past years of Bayesian analysis in many methodological and applied fields as th...
The emergence in the past years of Bayesian analysis in many methodological and applied fields as th...
International audienceThis chapter surveys the most standard Monte Carlo methods available for simul...
A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
Abstract: This chapter surveys advances in the field of Bayesian com-putation over the past twenty y...
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most i...