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Bayesian Posterior Predictive Probability Happiness
(Scientific Research Publishing, 2017)
Bayesian optimization of crystallization processes to guarantee end-use product properties
(Universidad Nacional del Sur, 2020-04-01)
For pharmaceutical solid products, the issue of reproducibly obtaining their desired end-use properties depending on crystal size and form is the main problem to be addressed and solved in process development. Lacking a ...
Inductive transfer for learning Bayesian networks
(Springer, 2009)
Bayesian estimation of performance measures of screening tests in the presence of covariates and absence of a gold standard
(Brazilian Statistical AssociationSao PauloBrasil, 2009)
Combining inconsistent data
(IEEE, 2007)
At present, the most widely used procedure for finding the value of a quantity from data obtained by different observers involves calculating the inverse-variance weighted mean of the observers' estimates. This method ...
Bayesian nonparametric estimation of test equating functions with covariates
(2015)
Equating is an important step in the process of collecting, analyzing, and reporting test scores in any program of assessment. Methods of equating utilize functions to transform scores on two or more versions of a test, ...
Disequilibrium Coefficient: A Bayesian Perspective
(BERKELEY ELECTRONIC PRESS, 2011)
Hardy-Weinberg Equilibrium (HWE) is an important genetic property that populations should have whenever they are not observing adverse situations as complete lack of panmixia, excess of mutations, excess of selection ...
A Bayesian decision theory approach for genomic selection
(Genetics Society of America, 2018)
Doctor, what does my positive test mean? From Bayesian textbook tasks to personalized risk communication
(2015)
Most of the research on Bayesian reasoning aims to answer theoretical questions about the extent to which people are able to update their beliefs according to Bayes' Theorem, about the evolutionary nature of Bayesian ...
Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data
(Crop Science Society of America, 2020)