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Sampling-Based Approaches to Calculating Marginal Densities
by
Smith, Adrian F. M.
, Gelfand, Alan E.
in
Algorithms
/ Approximation
/ Conditional probability structure
/ Data augmentation
/ Density
/ Density estimation
/ Estimators
/ Gibbs sampler
/ Hierarchical models
/ Importance sampling
/ Missing data
/ Monte Carlo sampling
/ Natural order
/ Posterior distributions
/ Random variables
/ Sampling
/ Sampling distributions
/ Statistical variance
/ Stochastic substitution
/ Tanneries
/ Theory and Methods
/ Variance components
1990
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Sampling-Based Approaches to Calculating Marginal Densities
by
Smith, Adrian F. M.
, Gelfand, Alan E.
in
Algorithms
/ Approximation
/ Conditional probability structure
/ Data augmentation
/ Density
/ Density estimation
/ Estimators
/ Gibbs sampler
/ Hierarchical models
/ Importance sampling
/ Missing data
/ Monte Carlo sampling
/ Natural order
/ Posterior distributions
/ Random variables
/ Sampling
/ Sampling distributions
/ Statistical variance
/ Stochastic substitution
/ Tanneries
/ Theory and Methods
/ Variance components
1990
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Sampling-Based Approaches to Calculating Marginal Densities
by
Smith, Adrian F. M.
, Gelfand, Alan E.
in
Algorithms
/ Approximation
/ Conditional probability structure
/ Data augmentation
/ Density
/ Density estimation
/ Estimators
/ Gibbs sampler
/ Hierarchical models
/ Importance sampling
/ Missing data
/ Monte Carlo sampling
/ Natural order
/ Posterior distributions
/ Random variables
/ Sampling
/ Sampling distributions
/ Statistical variance
/ Stochastic substitution
/ Tanneries
/ Theory and Methods
/ Variance components
1990
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Sampling-Based Approaches to Calculating Marginal Densities
Journal Article
Sampling-Based Approaches to Calculating Marginal Densities
1990
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Overview
Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions. The three approaches will be reviewed, compared, and contrasted in relation to various joint probability structures frequently encountered in applications. In particular, the relevance of the approaches to calculating Bayesian posterior densities for a variety of structured models will be discussed and illustrated.
Publisher
Taylor & Francis Group,American Statistical Association
Subject
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