Bayesian Data Analysis and Signal Processsing

Instructor:   Dr. Kevin Knuth
Contact:   kknuth@albany.edu PH 211, 442 - 4653
Office Hours:   Wed. 10:30am - 12:00pm
Teaching Assistant:  Deniz Gencaga

IMPORTANT NOTICE:
HW 6 is Online
Undergraduates not completing final projects are required to do this last assignment

HW 4p is Online
HW 4p is due Nov 13
Since HW 4p requires some derivations, there is no written HW this period.

HW 4 EXTRA CREDIT PROBLEMS 2 and 3 are online

HW 5w and HW5p are Online
These are the last required assignments.

READING ASSIGNMENTS

Source Separation
Knuth K.H. 1999. A Bayesian Approach to Source Separation, ICA99 Workshop, Aussois France..
Knuth K.H. 2005. Informed Source Separation, EUSIPCO 2005, Antalya Turkey.

Histogram Binning
Knuth K.H. Optimal Data-Based Binning for Histograms.

Experimental Design
Knuth K.H. et al. 2007.Designing Intelligent Instruments. In MaxEnt 2007 Proceedings
Knuth K.H. Computing with Questions. Talk at University at Albany on 30 Nov 2007.

Bayesics
Jaynes, E. T. 1986. Bayesian Methods: General Background. In Maximum-Entropy and Bayesian Methods in Applied Statistics, J. H. Justice (ed.), Cambridge Univ. Press, Cambridge, p. 1

GENERAL INFORMATION
Text: Data Analysis: A Bayesian Approach (2nd edition), D.S. Sivia and J. Skilling
Software: Matlab Release 14: Student Version

Course Description: Introduction to both the principles and practice of Bayesian and maximum entropy methods for data analysis, signal processing, and machine learning. This is a hands-on course that will introduce the use of the MATLAB computing language for software development. Students will learn to write their own Bayesian computer programs to solve problems relevant to physics, chemistry, biology, earth science, and signal processing, as well as hypothesis testing and error analysis. Optimization techniques to be covered include gradient ascent, fixed-point methods, and Markov chain Monte Carlo sampling techniques. 3 credits.