6 Day 6 (February 2)

6.1 Announcements

• Please read Ch. 6

• Assignment 3 is due Sunday Feb. 5

6.2 Introduction to Metropolis-Hastings algorithm

• Live illustration using example from homework 2 and 3
  • Illustrate different proposal distributions
  • Trace plots and acf plots
  • Illustrate burn-in, convergence, mixing, thinning, length

6.3 Our first statistical model

• The backstory
  • Bat
  • Rabies
• Building a statistical model using a likelihood-based (classical) approach
  • Specify (write out) the likelihood
  • Select an approach to estimate unknown parameters (e.g., maximum likelihood)
  • Quantify uncertainty in unknown parameters (e.g., using normal approximation)
• Building a statistical model using a Bayesian approach
  • Specify (write out) the likelihood/data model
  • Specify the parameter model (or prior) including hyper-parameters
  • Select an approach to obtain the posterior distribution
    • Analytically (i.e., pencil and paper)
    • Live example Metropolis-Hastings