Pensum

Chapter 1: Review
Chapter 2: Optimization and Solving Nonlinear Equations
- §2.1: Univariate Problems
- §2.2: Multivariate Problems
Chapter 3: Combinatorial Optimization
- §3.1: Hard Problems and NP-Completeness
- §3.2: Local Search
- §3.3: Simulated Annealing
- (possibly) §3.4: Genetic Algorithms
- (possibly) §3.5: Tabu Algorithm
Chapter 4: EM Optimization Methods
- §4.1: Missing Data, Marginalization, and Notation
- §4.2: The EM Algorithm
- §4.3: EM Variants
Chapter 6: Simulation and Monte Carlo Integration
- §6.1 Introduction to the Monte Carlo Method
- §6.2: Exact Simulation
- §6.3: Approximate Simulation
- §6.4: Variance Reduction Techniques
Chapter 7: Markov Chain Monte Carlo
- §7.1: Metropolis-Hastings Algorithm
- §7.2: Gibbs Sampling
- §7.3: Implementation
Chapter 8: Advanced Topics in MCMC
- (possibly) §8.1: Adaptive MCMC
- (possibly) §8.2: Reversible Jump MCMC
- (possibly) §8.6: Markov Chain Maximum Likelihood
Chapter 9: Bootstrapping
- §9.1: The Bootstrap principle
- §9.2: Basic Methods
- §9.3: Bootstrap Inference
- §9.4: Reducing Monte Carlo Error
- §9.5: Bootstrapping Dependent Data
- §9.6: Bootstrap Performance
- §9.7: Other Use of the Bootstrap
- (possibly) §9.8: Permutation Tests

Published Jan. 8, 2025 2:48 PM - Last modified Jan. 9, 2025 1:55 PM