Page 10 - Computational Statistics Handbook with MATLAB

xii Computational Statistics Handbook with MATLAB

Exercises

Chapter 11
Markov Chain Monte Carlo Methods
11.1 Introduction
11.2 Background
Bayesian Inference
Monte Carlo Integration
Markov Chains
Analyzing the Output
11.3 Metropolis-Hastings Algorithms
Metropolis-Hastings Sampler
Metropolis Sampler
Independence Sampler
Autoregressive Generating Density
11.4 The Gibbs Sampler
11.5 Convergence Monitoring
Gelman and Rubin Method
Raftery and Lewis Method
11.6 MATLAB Code
11.7 Further Reading
Exercises

Chapter 12
Spatial Statistics
12.1 Introduction
What Is Spatial Statistics?
Types of Spatial Data
Spatial Point Patterns
Complete Spatial Randomness
12.2 Visualizing Spatial Point Processes
12.3 Exploring First-order and Second-order Properties
Estimating the Intensity
Estimating the Spatial Dependence
Nearest Neighbor Distances - G and F Distributions
K-Function
12.4 Modeling Spatial Point Processes
Nearest Neighbor Distances
K-Function
12.5 Simulating Spatial Point Processes
Homogeneous Poisson Process
Binomial Process
Poisson Cluster Process
Inhibition Process
Strauss Process

© 2002 by Chapman & Hall/CRC