A simple procedure to calculate the Bayes factor between linked and pleiotropic QTL models is presented. The Bayes factor is calculated from the marginal prior and posterior densities of the locations of the QTL under a linkage and a pleiotropy model. The procedure is computed with a Gibbs sampler, and it can be easily applied to any model including the location of the QTL as a variable. The pr...

in this research, an iterative approach is employed to recognize and classify control chart patterns. to do this, by taking new observations on the quality characteristic under consideration, the maximum likelihood estimator of pattern parameters is first obtained and then the probability of each pattern is determined. then using bayes’ rule, probabilities are updated recursively. finally, when...

the objective of this study was to compare six statistical methods for prediction of genomic breedingvalues for traits with different genetic architecture in term of gene effects distributions and number ofquantitative traits loci (qtls). a genome consisted of 500 bi-allelic single nucleotide polymorphism(snp) markers distributed over a chromosomes with 100 cm length was simulated. three differ...

BACKGROUND Gangemi, Mancini, and van den Hout (2012) argued that anxious patients use safety behaviors as information that the situation in which the safety behaviors are displayed is dangerous, even when that situation is objectively safe. This was concluded from a vignette study in which anxious patients and non-clinical controls rated the dangerousness of scripts that were safe or dangerous ...

Uniformly most powerful tests are statistical hypothesis tests that provide the greatest power against a fixed null hypothesis among all tests of a given size. In this article, the notion of uniformly most powerful tests is extended to the Bayesian setting by defining uniformly most powerful Bayesian tests to be tests that maximize the probability that the Bayes factor, in favor of the alternat...

The learning curve of Bayes optimal classii-cation algorithm when learning a perceptron from noisy random training examples is calculated exactly in the limit of large training sample size and large instance space dimension using methods of statistical mechanics. It is shown that under certain assumptions, in this \thermodynamic" limit, the probability of misclassiication of Bayes optimal algor...

Empirical Bayes modeling has a long and celebrated history in statistical theory and applications. After a brief review of the literature, we propose a new dynamic empirical Bayes modeling approach which provides flexible and computationally efficient methods for the analysis and prediction of longitudinal data from many individuals. This dynamic empirical Bayes approach pools the cross-section...