Description
BAYESIAN SIGNAL PROCESSING
This course presents Bayesian methods for signal processing and estimation. Course topics include: Bayesian estimation, sampling theory, importance sampling, simulation-based Bayesian methods, classical and modern Bayesian state-space processors, and particle filters. Other topics of interest, like joint state/parameter estimation, discrete hidden Markov models, sequential detection, may also discussed, and various engineering-related applications are presented.
Details
Grading Basis
Grad Letter Grade
Units
3
Component
Lecture - Required
Offering
Course
ME 2243
Academic Group
Swanson School of Engineering
Academic Organization
Mechanical Engineering
Campus
Pittsburgh Campus
Typically Offered
Fall, Spring