Lecture 13-14. More on Bayesian Estimation
Learning Goal: You should know and be able to use the Cramer-Rao Bound to understand fundamental limits on how well you can learn parameters θ from data
y obtained with probability
p(y;θ). You should be able to describe and use the nonlinear Bayesian Filter and the Kalman filter. You should also be able to describe and use a particle filter.
Lecture Slides: Lecture1314_BayesianEstimation.pdf Download Lecture1314_BayesianEstimation.pdf
Lecture Video: LECTURE1314_FULL.mp4 Download LECTURE1314_FULL.mp4 (from 2020)
Also available on youtube here
Reading Assignment: Lecture notes on Bayesian Estimation andSe nsorFusion.pdf Download Lecture notes on Bayesian Estimation andSe nsorFusion.pdf
Code:
positionCRB.m Download positionCRB.m
gps_tradeoff.m Download gps_tradeoff.m
Please also check out the short video describing the particle filter available in the additional material:
Additional material - 13-14 Bayesian Estimation and Sensor Fusion