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  1. (DT) Segment 10: The Central Limit Theorem
  2. (DT) Segment 11: Random Deviates
  3. (DT) Segment 12: P-Value Tests
  4. (DT) Segment 13: The Yeast Genome
  5. (DT) Segment 14: Bayesian Criticism of p-values
  6. (DT) Segment 15: The Towne Family Revisited
  7. (DT) Segment 16: Multiple Hypotheses
  8. (DT) Segment 17: The Multivariate Normal Distribution
  9. (DT) Segment 18: The Covariance Matrix
  10. (DT) Segment 19: The Chi Square Statistic
  11. (DT) Segment 1: Let's talk about probability
  12. (DT) Segment 20: Nonlinear Least Squares Fitting
  13. (DT) Segment 21: Marginalize or Condition Uninteresting Fitted Parameters
  14. (DT) Segment 22: Uncertainty of Derived Parameters
  15. (DT) Segment 23: Bootstrap Estimation of Uncertainty
  16. (DT) Segment 24: Goodness of Fit
  17. (DT) Segment 27: Mixture Models
  18. (DT) Segment 28: Gaussian Mixture Models in 1D
  19. (DT) Segment 29: GMMs in N-Dimensions
  20. (DT) Segment 2: Bayes
  21. (DT) Segment 30: Expectation Maximization Methods
  22. (DT) Segment 31: A Tale of Model Selection
  23. (DT) Segment 32: Contingency Tables: A First Look
  24. (DT) Segment 33: Contingency Table Protocols and Exact Fisher Test
  25. (DT) Segment 34: Permutation Tests
  26. (DT) Segment 39: MCMC and Gibbs Sampling
  27. (DT) Segment 3: Monty Hall
  28. (DT) Segment 4: The Jailor's Tip
  29. (DT) Segment 5: Bernoulli Trials
  30. (DT) Segment 6: The Towne Family
  31. (DT) Segment 7: Central Tendency And Moments
  32. (DT) Segment 8: Characteristic Functions
  33. (DT) Segment 8: Some Standard Distributions
  34. (DT) Segment 9: Characteristic Functions
  35. (Rene) Segment 10: The central limit theorem
  36. (Rene) Segment 11: Random Deviates
  37. (Rene) Segment 12: P-value tests
  38. (Rene) Segment 13: The Yeast Genome
  39. (Rene) Segment 15: The Towne Family - again
  40. (Rene) Segment 17: The multivariate normal distribution
  41. (Rene) Segment 18: The Correlation Matrix
  42. (Rene) Segment 19: The ChiSquare statistic
  43. (Rene) Segment 1: Let's talk about probability
  44. (Rene) Segment 20: Non-linear least squares
  45. (Rene) Segment 21: Marginalize or condition uninteresting fitted parameters
  46. (Rene) Segment 22: Uncertainty of derived parameters
  47. (Rene) Segment 23: Bootstrap estimation of uncertainty
  48. (Rene) Segment 24: Goodness of fit
  49. (Rene) Segment 25: Mixture models
  50. (Rene) Segment 26: Gaussian Mixture models

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