1. Introduction -- 2. Normal Approximations to Likelihoods and to Posteriors -- 3. Nonnormal Approximations to Likelihoods and Posteriors -- 4. The EM Algorithm -- 5. The Data Augmentation Algorithm -- 6. Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm