Notes and Reading - Lecture #7

Lecture #7 looked at probabilistic reasoning over time, where the most general model is the dynamic Bayesian network.


Notes are available in two formats:

We also have notes taken by our official scribe.


The lecture covered the material from Chapter 15, especially Sections 15.1, 15.2, 15.3.1, 15.4.1, 15.5.

If you want more information than is contained in the textbook, then Kevin Murphy's thesis:

K. P. Murphy Dynamic Bayesian Networks: Representation, Inference and Learning, PhD Thesis, Department of Computer Science, UB Berkeley, 2002.
is the definitive work. The early parts, in particular, provide a nice succinct introduction to the area.