Bayesian Models for High-Dimensional Count Data with Feature Selection
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주제(키워드)
BAYESIAN
, BAYESIAN INFERENCE
, BAYESIAN NONPARAMETRIC APPROACHES
, Bayesian inference
, Bayesian nonparametric approaches
, CLUSTERING
, COUNT
, COUNT DATA
, Clustering
, Count data
, DATA
, DIMENSIONAL
, DIRICHLET PROCESS
, Dirichlet process
, FEATURE
, FEATURE SELECTION
, Feature selection
, HIGH
, HIGH-DIMENSIONAL DATA
, High-dimensional data
, INTEGRATIVE ANALYSIS
, Integrative analysis
, MODELS
, REGRESSION
, Regression
, SELECTION
, STATISTICS
, Statistics
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발행기관
Rice University.
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발행년도
2016
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학위명
박사
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학과 및 전공
Statistics
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UCI
I804:11009-000000150563
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DOI
10.23186/korea.000000150563.11009.0001061
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제출원본
PQDT10673582
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