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Prof. Vanja M. Dukic
Appm 7400-001 Spring 2012
http://amath.colorado.edu/courses/7400_001/2012Spr/
Bayesian Statistics and Computing
Professor: Vanja M. Dukic
email:
Vanja.Dukic (a t) colorado.edu
Office hours: MW 1:00pm-2:30pm,
Applied Math, ECOT 322
This class is an MS/PhD level class, but is intended to be practical
and catered to audiences from mixed disciplines. It will cover the
basics of Bayesian inference, modeling, and algorithms. It will begin
with the introduction to Bayesian statistics, and cover normal and
non-normal approximation to likelihood and posteriors, the EM
algorithm, data augmentation, and Markov chain Monte Carlo (MCMC)
methods. We will also cover some more advanced MCMC and particle
filtering algorithms towards the end of the semester. Multiple
Bayesian modeling examples (hierarchical linear, hierarchical
generalized linear, time series, hidden Markov, and state space
models) will be used throughout the course. There will be weekly
homeworks, and students will be expected to complete a data analysis
project by the end of the course. There will be one final in-class
presentation. Variety of programming languages (eg, Matlab, R, C, C++)
can be used to implement the algorithms and carry out data analyses.
Previous coursework in probability and statistics (statistical
modeling), as well programming experience are required.
Required text:
"Tools for Statistical Inference
", by Martin Tanner.
Grading policy:
Homework: 45%
Final presentation and project: 45%
Course Participation: 10%
Final presentations:: 20-min talks on Monday (4/30) and Wednesday (5/2) from 4:00-6:30pm. Room ECCR 1B51.
Final papers due by:: Saturday (5/5) at 5pm, via email (pdf files only please).
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