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Handbook of Markov Chain Monte Carlo
Constructions for Nonstationary Spatial Processes
other
Author(s):
Paul Sampson
Publication date
(Online):
August 16 2010
Publisher:
CRC Press
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HR-EBSD (High Resolution - Electron Back Scatter Diffraction)
Author and book information
Book Chapter
Publication date (Print):
March 19 2010
Publication date (Online):
August 16 2010
Pages
: 119-130
DOI:
10.1201/9781420072884-c9
SO-VID:
e31c44b2-39f1-4dd2-ad7d-622e6581fc22
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Book chapters
Inference from Simulations and Monitoring Convergence
Modeling Preference Changes via a Hidden Markov Item Response Theory Model
An MCMC-Based Analysis of a Multilevel Model for Functional MRI Data
Partially Collapsed Gibbs Sampling and Path-Adaptive Metropolis–Hastings in High-Energy Astrophysics
Perfection within Reach
Gaussian Random Field Models for Spatial Data
Index
Optimal Proposal Distributions and Adaptive MCMC
The Data Augmentation Algorithm
Applications of MCMC in Fisheries Science
Likelihood-Free MCMC
Introduction to Markov Chain Monte Carlo
Reversible Jump MCMC
Posterior Exploration for Computationally Intensive Forward Models
MCMC in Educational Research
A Short History of MCMC
Statistical Ecology
MCMC for State–Space Models
Spatial Point Processes
Implementing MCMC
MCMC in the Analysis of Genetic Data on Related Individuals
Model Comparison and Simulation for Hierarchical Models
MCMC Using Hamiltonian Dynamics
Importance Sampling, Simulated Tempering and Umbrella Sampling
Parallel Bayesian MCMC Imputation for Multiple Distributed Lag Models
pp. 107
Low-Rank Representations for Spatial Processes
pp. 119
Constructions for Nonstationary Spatial Processes
pp. 553
Estimation of the causal effects of time-varying exposures
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