JTW > Courses > STAT 489: Bayesian Methods of Data Analysis > Course Calendar
[Sitemap]

This table gives a tentative timetable for the course. Everything in it, including the dates of homeworks and exams, is subject to change.

Tuesday Þursday
Tuesday Þursday
Wk01 Jan 24

Part One: Bayesian Parameter Estimation

Bayesian Probability

Jan 26

Part One: Bayesian Parameter Estimation

Posterior Probability Distributions

Wk02 Jan 31

Part One: Bayesian Parameter Estimation

Bayesian Point & Interval Estimation; Gaussian Approximation; Problem Set 1 due

Feb 2

Part One: Bayesian Parameter Estimation

Reparametrization; Non-informative and Improper Priors; Inference about future observations

Wk03 Feb 7

Part One: Bayesian Parameter Estimation

Sampling from a posterior; Problem Set 2 due

Feb 9

Part One: Bayesian Parameter Estimation

Multiparameter posteriors; Gaussian approximation and Hessian Matrix

Wk04 Feb 14

Part One: Bayesian Parameter Estimation

Marginalization; Problem Set 3 due

Feb 16

Part One: Bayesian Parameter Estimation

Multinomial Distribution

Wk05 Feb 21

Part Two: Bayesian Model Selection

Bayes factor and Jaynesian Evidence; Problem Set 4 due

Feb 22

Review for Prelim Exam 1

Wk06 Feb 28

FIRST PRELIM EXAM

Mar 1

Part Two: Bayesian Model Selection

Posterior predictive checking

Wk07 Mar 7

Part Two: Bayesian Model Selection

Poisson Process Example

Mar 9

Part Two: Bayesian Model Selection

Linear Regression Example; Problem Set 5 due

Mar 14

No Class

(Spring Break)

Mar 16

No Class

(Spring Break)

Wk08 Mar 21

Part Three: Computational Methods

Importance sampling; Markov-Chain Monte Carlo (Metropolis Algorithm)

Mar 23

Part Three: Computational Methods

MCMC Demonstration and Example; Problem Set 6 due

Wk09 Mar 28

Part Three: Computational Methods

Gibbs Sampler

Mar 30

Part Three: Computational Methods

JAGS; Problem Set 7 due

Wk10 Apr 4

Part Three: Computational Methods

Maximum Entropy

Apr 6

Part Three: Computational Methods

Hamiltonian Monte Carlo; Problem Set 8 due

Wk11 Apr 11

Part Three: Computational Methods

STAN

Apr 13

Review for Prelim Exam 2; Problem Set 8½ due

Wk12 Apr 18

Project brainstorming session

Apr 20

SECOND PRELIM EXAM

Wk13 Apr 25

Part Four: Generalized Linear Models

Multivariate linear models

Apr 27

Part Four: Generalized Linear Models

Multivariate linear models Problem Set 9 due

Wk14 May 2

Part Four: Generalized Linear Models Logistic Regression

May 4

Part Four: Generalized Linear Models Logistic Regression Example

Problem Set 10 due

Wk15 May 9

Project presentations

May 11

Project presentations; Problem Set 11 due


Last Modified: 2018 April 11

Dr. John T. Whelan / john.whelan@astro.rit.edu / Professor, School of Mathematical Sciences & Center for Computational Relativity and Gravitation, Rochester Institute of Technology

The contents of this communication are the sole responsibility of Prof. John T. Whelan and do not necessarily represent the opinions or policies of RIT, SMS, or CCRG.

HTML 4.0 compliant CSS2 compliant