Joseph Chang

Joseph Chang's picture
Professor of Statistics and Data Science
Address: 
24 Hillhouse Ave, New Haven, CT 06511-6814, Room 211
203-432-0642
Research Interests: 
Probability, stochastic processes, sequential analysis, statistical genetics, evolution, bioinformatics.

Courses

STAT 230b / STAT 530b/ PLSC 530b Introductory Data Analysis

Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used.

Term: Spring
Day/Time: Tuesday & Thursday, 9:00-10:15

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. 
 

Term: Fall
Day/Time: Tuesday, Thursday 1:00pm - 2:15pm

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.

Term: Spring
Day/Time: TBA

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.

Term: Spring

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.

Term: Spring
Day/Time: Wednesday, Thursday 4:00pm - 5:15pm (tentative)