## 1. Discussion of MCMC

@T.L.Griffiths:2008:dd194 - Sec. 5.0 Markov Chain Monte Carlo (pp. 31-34)

#### Reading questions:

a) Under what conditions is it *not* necessary to use an approximate sampling method to solve a Bayesian equation?

b) What are the major differences between Gibbs sampling and Metropolis-Hastings sampling?

## 2. Particle filters

Particle Filters Explained without Equations

#### Viewing questions:

a) As the number of particles increases, what happens to a particle filterâ€™s accuracy? What happens to its run-time? Would you want an infinite number of particles? Why or why not?

b) Describe a phenomenon that particle filters be particularly good for modeling. Why do you think a particle filter would be helpful?

**Algorithms for Inference** For a somewhat longer, mathier disucssion of MCMC algorithms, see @andrieu2003introduction.