1. OVERVIEW OF BAYESIAN INFERENCE: PART 1

4. THE BAYESIAN APPROACH IN A NUTSHELL

7. HOW DOES ONE DO THIS?

8. HISTORICAL NOTES

10. Example: Infer mean of normal distribution with known variance

16. Example: model for allelic count with Beta prior

31. Mixtures of conjugate densities

32. Joint, Conditional and Marginal Posterior Distributions

33. Marginal posterior densities

34. Conditional posterior distributions

35. BAYESIAN ANALYSIS OF CLASSICAL LINEAR REGRESSION MODEL (normal distribution of residuals, “uninformative” priors)

45. 2) Model for allelic count with Beta prior

46. Binary Data: Predictive distribution

47. Example of predictive distribution: beta-binomial model with composition sampling

49. Bayes factor for 2 beta-binomial models

51. Posterior Predictive Distributions