This document summarizes key concepts from CS 221 lecture 5 on hidden Markov models and temporal filtering. The lecture covered Markov chains, hidden Markov models, and particle filtering for approximate inference in hidden Markov models. Hidden Markov models extend Markov chains to allow for hidden states that are observed indirectly through emissions. Particle filtering uses samples or "particles" to represent the distribution over hidden states and approximate inference.

1. CS 221: Artificial Intelligence Lecture 5: Hidden Markov Models and Temporal Filtering Sebastian Thrun and Peter Norvig Slide credit: Dan Klein, Michael Pfeiffer

2. Class-On-A-Slide X 5 X 2 E 1 X 1 X 3 X 4 E 2 E 3 E 4 E 5

3. Example: Minerva

4. Example: Robot Localization

5. Example: Groundhog

6. Example: Groundhog

7. Example: Groundhog

27. Inference in HMMs (Filtering) E 1 X 1 X 2 X 1

29. Example HMM

30. Example: HMMs in Robotics

32. Example: Robot Localization

38. Particle Filters

39. Sensor Information: Importance Sampling

40. Robot Motion

41. Sensor Information: Importance Sampling

42. Robot Motion

49. Time for Questions