Contents
Wiley Series in Computational Statistics
1.2 Taylor's Theorem and Mathematical Limit Theory
1.3 Statistical Notation and Probability Distributions
Chapter 2: Optimization and Solving Nonlinear Equations
Chapter 3: Combinatorial Optimization
3.1 Hard Problems and NP-Completeness
Chapter 4: Em Optimization Methods
4.1 Missing Data, Marginalization, and Notation
Part II: Integration and Simulation
Chapter 5: Numerical Integration
5.4 Frequently Encountered Problems
Chapter 6: Simulation and Monte Carlo Integration
6.1 Introduction to the Monte Carlo Method
6.4 Variance Reduction Techniques
Chapter 7: Markov Chain Monte Carlo
7.1 Metropolis–Hastings Algorithm
Chapter 8: Advanced Topics in MCMC
8.3 Auxiliary Variable Methods
8.4 Other Metropolis–Hastings Algorithms
8.6 Markov Chain Maximum Likelihood
8.7 Example: MCMC for Markov Random Fields
9.4 Reducing Monte Carlo Error
9.5 Bootstrapping Dependent Data
9.7 Other Uses of the Bootstrap
Part IV: Density Estimation and Smoothing
Chapter 10: Nonparametric Density Estimation
10.2 Kernel Density Estimation
Chapter 11: Bivariate Smoothing
11.3 Comparison of Linear Smoothers
Chapter 12: Multivariate Smoothing