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Volumn , Issue , 2012, Pages 323-330

Learning high-dimensional mixed graphical models with missing values

Author keywords

[No Author keywords available]

Indexed keywords

BIOMEDICAL FIELDS; BIOMEDICAL INSTRUMENTATION; COMPUTATIONAL BURDEN; CONTINUOUS VARIABLES; EXPECTATION-MAXIMIZATION ALGORITHMS; GRAPHICAL MODEL; HIGH DIMENSIONS; HIGH-DIMENSIONAL; MISSING AT RANDOMS; MISSING DATA PROBLEM; MISSING OBSERVATIONS; MISSING VALUES;

EID: 84874692432     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (4)

References (14)
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    • Didelez, V.1    Pigeot, I.2
  • 5
    • 76949106424 scopus 로고    scopus 로고
    • Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
    • D. Edwards, G. de Abreu, and R. Labouriau. 2010. Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests. BMC Bioinformatics, 11(1) :18.
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    • Edwards, D.1    De Abreu, G.2    Labouriau, R.3
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    • Graphical models for associations between variables, some of which are qualitative and some quantitative
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    • A second generation human haplotype map of over 3.1 million SNPs
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.