This course provides an introduction to the applied mathematical and statistical tools used frequently in astrophysics including modeling, data reduction, analysis, and computational astrophysics. Topics will include Special Functions, Differential Equations, Probability and Statistics, and Frequency Domain Analysis.

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

Continuation of Thesis

Dissertation research by the candidate for an appropriate topic as arranged between the candidate and the research advisor.

Continuation of Thesis

This course serves as an introduction to the mathematical tools needed to solve intermediate and upper-level physics problems. Topics include matrix algebra, vector calculus, Fourier analysis, partial differential equations in rectangular coordinates, and an introduction to series solutions of ordinary differential equations.

This course provides a comprehensive introduction to Bayesian statistical reasoning and methodology, emphasizing both theoretical foundations and practical applications. Students will learn how to formulate statistical models using prior distributions, update beliefs through observed data via Bayes’ theorem, and make inferences using posterior distributions. Topics include conjugate priors, linear models, hierarchical models, model comparison and selection, and computational methods such as Markov Chain Monte Carlo (MCMC) and Gibbs sampling. Students will also gain hands-on experience implementing Bayesian models in modern statistical software (e.g., R or Python).

This course provides a comprehensive introduction to Bayesian statistical reasoning and methodology, emphasizing both theoretical foundations and practical applications. Students will learn how to formulate statistical models using prior distributions, update beliefs through observed data via Bayes’ theorem, and make inferences using posterior distributions. Topics include conjugate priors, linear models, hierarchical models, model comparison and selection, and computational methods such as Markov Chain Monte Carlo (MCMC) and Gibbs sampling. Students will also gain hands-on experience implementing Bayesian models in modern statistical software (e.g., R or Python).