STAT 238a / STAT 538a Probability and Statistics
Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis. Essentials of probability, including conditional probability, random variables, distributions, law of large numbers, central limit theorem, and Markov chains. Statistical inference with emphasis on the Bayesian approach: parameter estimation, likelihood, prior and posterior distributions, Bayesian inference using Markov chain Monte Carlo. Introduction to regression and linear models. Computers are used for calculations, simulations, and analysis of data.
After MATH 118 or 120.
STAT 490b Statistics Senior Seminar Project
Under the supervision of a member of the faculty, each student works on an independent project. Students participate in seminar meetings at which they speak on the progress of their projects.
Permission required. No final Exam.
STAT 645b / BIS 692b / CB&B 645b Statistical Methods in Genetic and Bioinformatics
Introduction to problems, algorithms, and data analysis approaches in computational biology and bioinformatics; stochastic modeling and statistical methods applied to problems such as mapping disease-associated genes, analyzing gene expression microarray data, sequence alignment, and SNP analysis. Statistical methods include maximum likelihood, EM, Bayesian inference, Markov chain Monte Carlo, and some methods of classification and clustering; models include hidden Markov models, Bayesian networks, and the coalescent. The limitations of current models, and the future opportunities for model building, are critically addressed.
STAT 654b Topics in Bayesian Inference and Data Analysis
Topics in the theory and practice of Bayesian statistical inference, ranging from a review of fundamentals to questions of current research interest. Motivation for the Bayesian approach, Bayesian computation, Monte Carlo methods, use of software (including R, BUGS, and possibly others), asymptotics, model checking and comparison, empirical Bayes approaches, hierarchical models, and Bayesian nonparametrics. A selection of other topics as time permits; possibilities include Bayesian design, variational methods, and approximate Bayesian computation. Assumed background includes probability and statistics at least at the level of STAT 541 and 542, Markov Chains as covered in STAT 551, and computing in R.