STATS 366: Modern Statistics for Modern Biology (BIOS 221)
Application based course in nonparametric statistics. Modern toolbox of visualization and statistical methods for the analysis of data, examples drawn from immunology, microbiology, cancer research and ecology. Methods covered include multivariate methods (PCA and extensions), sparse representations (trees, networks, contingency tables) as well as nonparametric testing (Bootstrap, permutation and Monte Carlo methods). Hands on, use R and cover many Bioconductor packages. Prerequisite: Minimal familiarity with computers. Instructor consent. Location: Li Ka Shing Center, room 120.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
STATS 367: Statistical Models in Genetics
Statistical problems in association and linkage analysis of qualitative and quantitative traits in human and experimental populations; sequence alignment and analysis; population genetics/evolution (WrightFisher model, Kingman coalescent, models of nucleotide substitution); related computational algorithms. Prerequisites: knowledge of probability through elementary stochastic processes and statistics through likelihood theory.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
STATS 370: Bayesian Statistics I (STATS 270)
This is the first of a two course sequence on modern Bayesian statistics. Topics covered include: real world examples of large scale Bayesian analysis; basic tools (models, conjugate priors and their mixtures); Bayesian estimates, tests and credible intervals; foundations (axioms, exchangeability, likelihood principle); Bayesian computations (Gibbs sampler, data augmentation, etc.); prior specification. Prerequisites: statistics and probability at the level of
Stats300A,
Stats305, and
Stats310.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
STATS 371: Bayesian Statistics II (STATS 271)
This is the second of a two course sequence on modern Bayesian statistics. Topics covered include: Asymptotic properties of Bayesian procedures and consistency (Doobs theorem, frequentists consistency, counter examples); connections between Bayesian methods and classical methods (the complete class theorem); generalization of exchangeability; general versions of the Bayes theorem in the undominated case; non parametric Bayesian methods (Dirichelet and Polya tree priors). Throughout general theory will be illustrated with classical examples. Prerequisites:
Stats 270/370.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
STATS 374: Large Deviations Theory (MATH 234)
Combinatorial estimates and the method of types. Large deviation probabilities for partial sums and for empirical distributions, Cramer's and Sanov's theorems and their Markov extensions. Applications in statistics, information theory, and statistical mechanics. Prerequisite:
MATH 230A or
STATS 310. Offered every 23 years.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
STATS 375: Inference in Graphical Models
Graphical models as a unifying framework for describing the statistical relationships between large sets of variables; computing the marginal distribution of one or a few such variables. Focus is on sparse graphical structures, lowcomplexity algorithms, and their analysis. Topics include: variational inference; message passing algorithms; belief propagation; generalized belief propagation; survey propagation. Analysis techniques: correlation decay; distributional recursions. Applications from engineering, computer science, and statistics. Prerequisite:
EE 278,
STATS 116, or
CS 228. Recommended:
EE 376A or
STATS 217.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
STATS 376A: Information Theory (EE 376A)
The fundamental ideas of information theory. Entropy and intrinsic randomness. Data compression to the entropy limit. Huffman coding. Arithmetic coding. Channel capacity, the communication limit. Gaussian channels. Kolmogorov complexity. Asymptotic equipartition property. Information theory and Kelly gambling. Applications to communication and data compression. Prerequisite: EE178 or
STATS 116, or equivalent.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Weissman, T. (PI)
STATS 376B: Network Information Theory (EE 376B)
Network information theory deals with the fundamental limits on information flow in networks and the optimal coding schemes that achieve these limits. It aims to extend Shannon's pointtopoint information theory and the FordFulkerson maxflow mincut theorem to networks with multiple sources and destinations. The course presents the basic results and tools in the field in a simple and unified manner. Topics covered include: multiple access channels, broadcast channels, interference channels, channels with state, distributed source coding, multiple description coding, network coding, relay channels, interactive communication, and noisy network coding. Prerequisites:
EE376A.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
STATS 390: Consulting Workshop
Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's dropin consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term consulting. May be repeated for credit. Prerequisites: course work in applied statistics or data analysis, and consent of instructor.
Terms: Aut, Win, Spr, Sum

Units: 13

Repeatable for credit

Grading: Satisfactory/No Credit
STATS 396: Research Workshop in Computational Biology
Applications of Computational Statistics and Data Mining to Biological Data. Attendance mandatory. Instructor approval required.
Terms: Aut, Win, Spr

Units: 12

Repeatable for credit

Grading: Satisfactory/No Credit
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