Informatics and Computing
Term : Fall 2018
Catalog Year : 2018-2019

INF 626 - Applied Bayesian Modeling

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Description: Bayesian statistical methods for analyzing data, with emphasis on ecological and biological data. Includes Bayes rule, basic Bayesian formulation (priors, posteriors, likelihoods), single- and multiple-parameter models, hierarchical models, generalized linear models, multivariate models, mixture models, models for missing data, merging statistical and process models, overview of spatial and temporal processes, and introduction to computation methods. Letter grade only.

Units: 3

No sections currently offered.

Prerequisite: Graduate Status