cancer registration is an important source for measuring the burden of cancer in a population. in practice, however, quite frequently incorrect patients are registered or data items can be inaccurately recorded or not recorded at all. also the process or quality of these registrations varies among countries. in this paper, we briefly discussed some statistical techniques including; mortality an...

We propose a default Bayesian hypothesis test for the presence of a correlation or a partial correlation. The test is a direct application of Bayesian techniques for variable selection in regression models. The test is easy to apply and yields practical advantages that the standard frequentist tests lack; in particular, the Bayesian test can quantify evidence in favor of the null hypothesis and...

Machine learning techniques are becoming increasingly useful in the study of complex biological phenomena. As more is understood about biological regulation, and additional experimental methods are developed, it is also becoming feasible to attempt to “reverse engineer” the pathways regulating biological systems using graphical models, with an emphasis on Bayesian networks. Herein we describe t...

While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become as widely accepted within the medical community as logistic regression models. We compare first-order logistic regression and naive Bayesian classification in the domain of reproductive medicine and demonstrate that the two techniques can result in models of com...

During the last several decades, the Bayesian networks have turned into a dynamic area of research. This great interest is owning to the advantages offered by special structure of Bayesian networks, which allows them to be very efficient in modeling domains with inherent uncertainty. Bayesian networks techniques can be successfully applied to mining various types of biomedical data. This chapte...

D. Trafimow (2003) presented an analysis of null hypothesis significance testing (NHST) using Bayes's theorem. Among other points, he concluded that NHST is logically invalid, but that logically valid Bayesian analyses are often not possible. The latter conclusion reflects a fundamental misunderstanding of the nature of Bayesian inference. This view needs correction, because Bayesian methods ha...