STAT 563 - Statistical Modeling and Computation

3 Credit Hours

Introduces foundational concepts in probability theory and statistical modeling. Topics covered include probability models, random variables, and key probability distributions. Students will explore the concept of likelihood, log-likelihood, score functions, and Fisher information, along with likelihood-based methods for estimation and parameter learning. Will emphasize numerical optimization techniques and likelihood-based hypothesis testing. Model fitting and selection are addressed using criteria such as AIC, SBC, WAIC, and ICOMP. Students will learn Monte Carlo inference, density estimation, and resampling techniques including bootstrap methods. Will also cover interval estimation, deviance-based confidence intervals, and an introduction to Bayesian estimation and inference. Estimation and inference for a variety of advanced statistical models are included, along with AI-assisted computational methods.

Software & Tools: MATLAB, R, or Python, with a focus on efficient computational practices.
Recommended Background: Mathematics 241.
Registration Restriction(s): Minimum student level - graduate.

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