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Bayesian kernel mixtures for counts. Antonio Canale & David B. Dunson Presented by Yingjian Wang Apr. 29, 2011. Outline. Existed models for counts and their drawbacks; Univariate rounded kernel mixture priors; Simulation of the univariate model; - PowerPoint PPT Presentation
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Bayesian kernel mixtures for counts
Antonio Canale & David B. DunsonPresented by Yingjian Wang
Apr. 29, 2011
• Existed models for counts and their drawbacks;
• Univariate rounded kernel mixture priors;
• Simulation of the univariate model;• Multivariate rounded kernel mixture
priors;• Experiment with the multivariate model;
Outline
Modeling of counts
• Mixture of Poissons:
a) Not a nonparametric way; b) Only accounts for cases where the
variance is greater than the mean;
Modeling of counts (2)
• DP mixture of Poissons/Multinomial kernel:
a) It is non-parametric but, still has the problem of not suitable for under-disperse cases;
b) If with multinomial kernel, the dimension of the probability vector is equal to the number of support points, causes overfitting.
4
Modeling of counts (3)
• DP with Poisson base measure:
a) There is no allowance for smooth deviations from the base;
• Motivation: The continuous densities can be accurately approximated using Gaussian kernels.
• Idea: Use kernels induced through rounding of continuous kernels.
5
Univariate rounded kernel
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*
discrete : ~
( ) ( )
continuous: ~
y p
g h
y f
Univariate rounded kernel (2)
• Existence:
• Consistence: (the mapping g(.) maintains KL neighborhoods.)
7
Examples of rounded kernels
• Rounded Gaussian kernel:
• Other kernels: log-normal, gamma, Weibull densities.
8
Eliciting the thresholds
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A Gibbs sampling algorithm
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Experiment with univariate model• Two scenarios:
• Two standards:• Results:
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Extension to multivariate model
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Telecommunication data• Data from 2050 SIM cards, with multivariate: yi=[yi1, yi2, yi3, yi4, yi5], Compare the RMG with generalized additive model (GAM):
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