Bayesian Inference
STAT 544 Winter Quarter 2018

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UW Statistics

Announcements
  • Information about the final mini-projects and the list of papers.
  • Projects and optionally Homework 6 will be due on Wednesday 3/14.
  • Possible topics for last week of classes VOTE HERE before 11am, Tuesday 3/6
    • Non-parametric Bayes:"mixtures" with variable K (Dirichlet process mixtures, Indian Buffet process,...)
    • Conjugate priors over combinatorial objects (tree graphical models, permutations): if you like combinatorics this is for you
    • Bayesian Model Averaging: how do Bayesians do "model selection"?
    • Sampling: Metropolis Hastings and more MCMC
    The last two topics are somewhat shorter than the first two.

What will the course be about?
The class will teach the basic principles of Bayesian Statistics, and will illustrate them with the study of a variety of models, problems and methods. See also the syllabus.

Who is this class for?
This class is intended primarily for PhD and MS students in statistics and related fields. Capacity permitting, the class is open to other graduate students with an interest in statistics, algorithms and computing who satisfy the prerequisites (see web page).

Grading:The grade is based (approximately) on homework + quizzes (70%), project (20%), and class participation (10%). The homework will contain both problems and implementation assignements. The project will consist of implementation, write-up and (poster?) presentation. The presentation will be during the last week of classes.

Prerequisites

  • A course in probability, including basic of multivariate analysis (conditional probability, independence, marginals, expectation, variance in multivariate seeting)
  • Fundamentals of statistics: Maximum Likelihood Estimation, MAP estimation, priors, likelihood, estimating parameters of usual distributions (normal, multinomial), Bayes' formula
  • Calculus and linear algebra: partial derivatives, gradient, the chain rule, vectors and matrices, matrix multiplications, eigenvalues and eigenvectors, positive definite matrices
  • Algorithms and data structure at a basic level (arrays, lists, sets, O( ) notation).
  • Medium (beyond beginner) ability with a computer programming language (like C, C++, Java or Matlab, python, R) at the level of STAT 534

Instructor: Marina Meila   mmp at stat dot wa**** dot edu

Lectures: Tuesdays, Thursdays 12:30 - 1:20, & Thursdays 1:30 -- 2:30 in PDL C-301

Office hours: Monday 2-3pm in PDL B-321

Course home page: http://www.stat.washington.edu/courses/stat544/winter18 (this page)

Class mailing list: stat544a_wi18 at UW