The problem is how to compare the quality of different hypothesis tests in a Bayesian framework without introducing a loss function. Three different linear orders on the set of all possible hypothesis tests are studied. The most natural order estimates the Fisher information between indicators of event and decision
A fundamental task in machine learning is to compare the performance of multiple algorithms. This is...
Toward a Bayesian Decision-Theoretic 2 The testing of null hypotheses is a methodologically limited ...
In modern statistical and machine learning applications, there is an increasing need for developing ...
Abstract. Most hypothesis testing in machine learning is done using the frequentist null-hypothesis ...
In Bayesian decision theory, the performance of an action is measured by its pos- terior expected lo...
We consider hypothesis testing problems with skewed alternatives via a Bayesian decision theoretic f...
We consider a hypothesis problem with directional alternatives. We approach the problem from a Bayes...
While the Bayesian parameter estimation has gained a wider acknowledgement among political scientist...
Hypothesis testing is a special form of model selection. Once a pair of competing models is fully de...
This dissertation consists of five chapters with three distinct but related research projects. In Ch...
In this paper, the performance of six types of techniques for comparisons of means is examined. Thes...
The article focuses on the discussion of basic approaches to hypotheses testing, which are Fisher, J...
Summary. We examine philosophical problems and sampling deficiencies that are associated with curren...
In the last sixty years there have been several attempts to build a measure of evidence that covers,...
Abstract: Hypothesis testing using Bayes factors (BFs) is known to suffer from several problems in t...
A fundamental task in machine learning is to compare the performance of multiple algorithms. This is...
Toward a Bayesian Decision-Theoretic 2 The testing of null hypotheses is a methodologically limited ...
In modern statistical and machine learning applications, there is an increasing need for developing ...
Abstract. Most hypothesis testing in machine learning is done using the frequentist null-hypothesis ...
In Bayesian decision theory, the performance of an action is measured by its pos- terior expected lo...
We consider hypothesis testing problems with skewed alternatives via a Bayesian decision theoretic f...
We consider a hypothesis problem with directional alternatives. We approach the problem from a Bayes...
While the Bayesian parameter estimation has gained a wider acknowledgement among political scientist...
Hypothesis testing is a special form of model selection. Once a pair of competing models is fully de...
This dissertation consists of five chapters with three distinct but related research projects. In Ch...
In this paper, the performance of six types of techniques for comparisons of means is examined. Thes...
The article focuses on the discussion of basic approaches to hypotheses testing, which are Fisher, J...
Summary. We examine philosophical problems and sampling deficiencies that are associated with curren...
In the last sixty years there have been several attempts to build a measure of evidence that covers,...
Abstract: Hypothesis testing using Bayes factors (BFs) is known to suffer from several problems in t...
A fundamental task in machine learning is to compare the performance of multiple algorithms. This is...
Toward a Bayesian Decision-Theoretic 2 The testing of null hypotheses is a methodologically limited ...
In modern statistical and machine learning applications, there is an increasing need for developing ...