CausalTrail: Testing hypothesis using causal Bayesian networks

CausalTrail: Testing hypothesis using causal Bayesian networks

Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system, caused by e.g. gene knockouts or an administered drug. Whereas numerous packages for constructing causal Bayesian networks are available, har...

Bayesian Fuzzy Hypothesis Testing with Imprecise Prior Distribution

This paper considers the testing of fuzzy hypotheses on the basis of a Bayesian approach. For this, using a notion of prior distribution with interval or fuzzy-valued parameters, we extend a concept of posterior probability of a fuzzy hypothesis. Some of its properties are also put into investigation. The feasibility and effectiveness of the proposed methods are also cla...

LINEAR HYPOTHESIS TESTING USING DLR METRIC

Several practical problems of hypotheses testing can be under a general linear model analysis of variance which would be examined. In analysis of variance, when the response random variable Y , has linear relationship with several random variables X, another important model as analysis of covariance can be used. In this paper, assuming that Y is fuzzy and using DLR metric, a method for testing ...

Using Bayesian Inference in Classical Hypothesis Testing

When students obtain a statistically significant sample at a 5% level of significance, they may conclude they can be 95% confident that the alternate hypothesis is true. From a classical perspective, this conclusion is unwarranted. Two elements may inadvertently support this unwarranted conclusion: the traditional definitions of Type I error and alpha, and the silence about confidence. From a B...

Bayesian Hypothesis Testing 1 Running head: BAYESIAN HYPOTHESIS TESTING The Need for Bayesian Hypothesis Testing in Psychological Science