Intro
Admin Working on Assignment #3 grading Remainder of the course is going to be A GMTK assignment Either a particle filter implementation or a paper presentation Review Let's look at a more general structure for the graphical model Bayesian Network Not time dependent Observed variables Unobserved variables Absence of links is where you find efficiencies Links themselves have probabilities associated with them. Bring it back to the case of the Markov Models Bring it back to the case of the Hidden Markov Models Introduce the idea of parameter tying Introduce Dynamic Bayesian Network Create a model of movement through a building that is a DBN New Material Worksheet GMTK Slides Assignment#4