Hypothesis testing and model choice are quintessential questions for statistical inference and while the Bayesian paradigm seems ideally suited for answering these questions, it faces difficulties of its own ranging from prior modeling to calibration, to numerical implementation. This chapter reviews these difficulties, from a subjective and personal perspective
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ...
A substantial school in the philosophy of science identifies Bayesian inference with inductive infer...
Hypothesis testing is a model selection problem for which the solution proposed by the two main stat...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
This note is a discussion on the perceived shortcomings of the classical Bayesian approach to testi...
Hypothesis testing is a special form of model selection. Once a pair of competing models is fully de...
We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradi...
While the Bayesian parameter estimation has gained a wider acknowledgement among political scientist...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
The Bayesian approach to discovery is essentially the Bayesian approach tohypothesis testing. This i...
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular a...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Abstract: The use of Bayesian analysis and debates involving Bayesian analysis are increasing for co...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ...
A substantial school in the philosophy of science identifies Bayesian inference with inductive infer...
Hypothesis testing is a model selection problem for which the solution proposed by the two main stat...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
Modern statistical software and machine learning libraries are enabling semi-automated statistical i...
This note is a discussion on the perceived shortcomings of the classical Bayesian approach to testi...
Hypothesis testing is a special form of model selection. Once a pair of competing models is fully de...
We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradi...
While the Bayesian parameter estimation has gained a wider acknowledgement among political scientist...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
The Bayesian approach to discovery is essentially the Bayesian approach tohypothesis testing. This i...
Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular a...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Abstract: The use of Bayesian analysis and debates involving Bayesian analysis are increasing for co...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ...
A substantial school in the philosophy of science identifies Bayesian inference with inductive infer...
Hypothesis testing is a model selection problem for which the solution proposed by the two main stat...
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