نتایج جستجو برای: bayesian method
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At the present time it is often stated that the maximum likelihood or the Bayesian method of phylogenetic construction is more accurate than the neighbor joining (NJ) method. Our computer simulations, however, have shown that the converse is true if we use p distance in the NJ procedure and the criterion of obtaining the true tree (Pc expressed as a percentage) or the combined quantity (c) of a...
Introduction: The Frequency-based method is commonly used to estimate the Net Reclassification Improvement (NRI)- and Integrated Discrimination Improvement (IDI) indices. These indices measure the magnitude of the performance of statistical models when a new biomarker is added. This method has poor performance in some cases, especially in small samples. In this study, the performance of two Bay...
due to extraordinary large amount of information and daily sharp increasing claimant for ui benefits and because of serious constraint of financial barriers, the importance of handling fraud detection in order to discover, control and predict fraudulent claims is inevitable. we use the most appropriate data mining methodology, methods, techniques and tools to extract knowledge or insights from ...
longitudinal study plays an important role in the epidemiological, clinical, and social science studies. in these kinds of studies, every individual is observed frequently during a period of time. the statistical analysis of longitudinal presents special opportunities and challenges. the repeated outcomes for one individual tend to be correlated among themselves also one of the pro...
Background and objective: Disease or mortality mapping are statistical methods aimed at providing precise estimates of rates across geographical maps. The aim of this research is to improve the precision of relative risk (RR) estimates of infant mortality (IM) for different rural areas, using empirical and full Bayesian methods. Methods: Infant mortality data were extracted from the vital ...
This paper examines the "K2" network scoring metric of Cooper and Herskovits. It shows counterintuitive results from applying this metric to simple networks. One family of noninformative priors is suggested for assigning equal scores to equivalent networks.
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