Course Calendar
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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.

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

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