CPS196 - Fall 1999
Hidden Markov Models
Background: Like MDPs, Hidden Markov Models (HMMs) are a finite-state
model with probabilistic transitions. However, whereas MDPs have a
notion of actions and rewards, HMMs have a notion of "observations".
We'll talk about how HMMs provide a well reasoned way of dealing with
state uncertainty, and how they are applied in some aspects of
language processing. We'll work through a "leet speak" repair
example.
Questions:
- Exercises 10.4-10.7 in the reading.
Notes
Modified: Wed Oct 27 21:01:46 EDT 1999
by Michael Littman, mlittman@cs.duke.edu