1. Chapter 15 Probabilistic Reasoning over Time

2. Chapter 15, Sections 1-5 Outline Time and uncertainty Inference: ltering, prediction, smoothing Hidden Markov models Kalman lters (a brief mention) Dynamic Bayesian networks Particle ltering

3. Time and uncertainty The world changes; we need to track and predict it Diabetes management vs vehicle diagnosis Basic idea: copy state and evidence variables for each time step

4. Markov processes (Markov chains)

5. Example

6. Inference tasks

7. Filtering

8. Filtering example

9. Smoothing

10. Smoothing example

11. Most likely explanation

12. Viterbi example

13. Hidden Markov models

14. Country dance algorithm

24. Kalman lters

25. Updating Gaussian distributions

26. Simple 1-D example

27. General Kalman update

28. 2-D tracking example: ltering

29. 2-D tracking example: smoothing

30. Where it breaks

31. Dynamic Bayesian networks

32. DBNs vs. HMMs

33. DBNs vs Kalman lters

34. Exact inference in DBNs

35. Likelihood weighting for DBNs

36. Particle ltering

37. Particle ltering contd.

38. Particle ltering performance

39. Chapter 15, Sections 1-5 Summary

40. Chapter 15, Section 6 Outline Speech as probabilistic inference Speech sounds Word pronunciation Word sequences

41. Speech as probabilistic inference

42. Phones

43. Speech sounds

44. Phone models

45. Phone model example

46. Word pronunciation models

47. Isolated words

48. Continuous speech

49. Language model

50. Combined HMM

51. DBNs for speech recognition

52. Chapter 15, Section 6 Summary Since the mid-1970s, speech recognition has been formulated as probabilistic inference Evidence = speech signal, hidden variables = word and phone sequences "Context" effects (coarticulation etc.) are handled by augmenting state Variability in human speech (speed, timbre, etc., etc.) and background noise make continuous speech recognition in real settings an open problem