Add to ORKG12 pages, 4 figures, submitted for the proceedings of MaxEnt 2009In this note, we shortly survey some recent approaches on the approximation of the Bayes factor used in Bayesian hypothesis testing and in Bayesian model choice. In particular, we reassess importance sampling, harmonic mean sampling, and nested sampling from a unified perspective
In general, model selection is an important prelude to subsequent statistical inference in risk asse...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account fo...
12 pages, 4 figures, submitted for the proceedings of MaxEnt 2009In this note, we shortly survey som...
International audienceThis paper surveys some well-established approaches on the approximation of Ba...
This paper surveys some well-established approaches on the approximation of Bayes factors used in Ba...
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
In modern statistical and machine learning applications, there is an increasing need for developing ...
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...
Since its introduction in the early 90's, the idea of using importance sampling (IS) with Markov cha...
Motivation: There often are many alternative models of a biochemical system. Distinguishing models a...
In general, model selection is an important prelude to subsequent statistical inference in risk asse...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account fo...
12 pages, 4 figures, submitted for the proceedings of MaxEnt 2009In this note, we shortly survey som...
International audienceThis paper surveys some well-established approaches on the approximation of Ba...
This paper surveys some well-established approaches on the approximation of Bayes factors used in Ba...
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computat...
While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the curren...
In modern statistical and machine learning applications, there is an increasing need for developing ...
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...
Since its introduction in the early 90's, the idea of using importance sampling (IS) with Markov cha...
Motivation: There often are many alternative models of a biochemical system. Distinguishing models a...
In general, model selection is an important prelude to subsequent statistical inference in risk asse...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
Purpose: Bayesian inference has become popular. It offers several pragmatic approaches to account fo...