bayesian density regression
DESCRIPTION
Bayesian Density Regression. Author: David B. Dunson and Natesh Pillai Presenter: Ya Xue April 28, 2006. Outline. Key idea Proof Application to HME. Bayesian Density Regression with Standard DP. The regression model: (i=1,...,n) Two cases:. Parametric model. - PowerPoint PPT PresentationTRANSCRIPT
Bayesian Density Regression
Author: David B. Dunson and Natesh Pillai
Presenter: Ya Xue
April 28, 2006
Outline
• Key idea
• Proof
• Application to HME
Bayesian Density Regression with Standard DP
• The regression model: (i=1,...,n)
• Two cases:
1.
2.
Parametric model
Standard Dirichlet process mixture model
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Bayesian Density Regression with Standard DP
• Model
• The algorithm automatically finds the shrinkage of parameters
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Polya Urn Model
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1()
1(),,|( 0
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• Standard Polya urn model
• This paper proposed a generalized Polya urn model.
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ii jw
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where is a kernel function.),( jiij xxww 0ijw monotonically as increases.),( ji xxd
.1lim ijxx wij
(1)
Idea – Spatial DPEquation (1) implies• The prior probability of setting decreases a
s increases.
• The prior probability of increases as more neighbors are added that have predictor values xj close to xi.
• The expected prior probability of increases in proportion to the hyperparameter .
ji ),( ji xxd
)(ii
)(ii
Outline
• Key idea
• Proof
• Application to HME
Spatial Varying Regression Model
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)(),|()|(
• At a given location in the feature space,
A mixture of an innovation random measure
and neighboring random measures
j~i indexes samples
Theorem 1
Hierarchical Model
• The hierarchical form
• Let denote an index set for the subjects drawn from the jth mixture component, for j=1,...,n. Then we have for
• Conditioning on Z, we can use the Polya urn result to obtain the conditional prior
• Only the subvector of elements of belonging to are informative.
Conditional Distribution},...,1{}:{ njZiI ij
*~jxi G
.jIi
(2))(i
iZI
Marginalize over Z
• We obtain the following generalization of the Polya urn scheme
(a)
(b)if sample i and j belong to the same mixture component.1ijm
Example
(a) (b)
For example, n=4,
p(mi)
Rewrite Equation (2)
• Let
• Then Eqn.(2) can be expressed as
(3)
Theorem 4
Hence, Eqn. (3) is equivalent to
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ij
ij
ijij
ii jw
wG
wBX .)()(),,,|( 0
)(
Predictive distribution
Outline
• Key idea
• Proof
• Application to HME
Mixture Model
• We simulate data from a mixture of two normal linear regression models
• Poor results obtained by using the standard DP mixture model.
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