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Bayesian Data Analysis
STAT 7330
School of Natural Sciences and Mathematics
Bayesian modeling fundamentals; prior distributions; large-sample theory and connection with classical inference; model checking and evaluation; Markov chain Monte Carlo methods, including Gibbs, Metropolis and related algorithms; convergence diagnostics; approximation of posterior mode and posterior density; single and multiparameter models such as those based on binomial, Poisson and normal distributions; regression models, including linear models, hierarchical linear models, generalized linear models, and basis function models; models for missing data; and implementation of methods using a software package. 3 credit hours.
Prerequisite: STAT 6337 or instructor consent required.
Offering Frequency: Every two years
Grades: 53
Median GPA: A
Mean GPA: 3.673
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Bayesian Data Analysis
STAT 7330
School of Natural Sciences and Mathematics
Bayesian modeling fundamentals; prior distributions; large-sample theory and connection with classical inference; model checking and evaluation; Markov chain Monte Carlo methods, including Gibbs, Metropolis and related algorithms; convergence diagnostics; approximation of posterior mode and posterior density; single and multiparameter models such as those based on binomial, Poisson and normal distributions; regression models, including linear models, hierarchical linear models, generalized linear models, and basis function models; models for missing data; and implementation of methods using a software package. 3 credit hours.
Prerequisite: STAT 6337 or instructor consent required.
Offering Frequency: Every two years
Grades: 53
Median GPA: A
Mean GPA: 3.673
Click a checkbox to add something to compare.