Fitting Statistical models to data with many more variables than ...

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A sparsity priorDraw ν from p( ν ) Gamma( b, k) , 0 < k ≤ 12 2iiDraw β from p β ν N ν2 2i(i|i) (0,i)p = ∫ p p d2 2 2( βi ) ( βi | νi) ( νi) νi(1)ν2iExplicitlyδ δ βp( β ) = ( ) , = (2)i(0.5 −k)2 K0.5−k( |i|) 2δ(0.5 −k)π Γ( k)( δ | βi|)b
The prior - special casesk=1 (Lasso, Tibshirani, 1996)p( β ) = ( δ / 2)exp( δ | β |)iik=0p( β ) ∝ δ exp{ −δ | β |}/ δ | β |i i ik=0, δ= 0(Jeffreys hyperprior, Figueiredo, 2001)p( β ) ∝ 1/ | β |ii
Page 2: Talk outline1. Introduction, motiva
Page 5: IntroductionResponse ModellingEach
Page 9 and 10: Some issues1. Artefacts - apparent
Page 11 and 12: Cross validationBoxplotC ross valid
Page 13: Parameters in the modelsThe models
Page 21 and 22: An EM algorithmWant to find (local)
Page 23 and 24: E- step continuedThe conditional ex
Page 25 and 26: CommentsStructure can be used to ma
Page 27 and 28: GeneRave in Action
Page 29 and 30: Êÿëáÿúÿð ÿðàçèñèíä
Page 31 and 32: Example 1: St Jude’s Leukaemia da
Page 33 and 34: Single Nucleotide PolymorphismsSNPA
Page 35 and 36: Data and modelWe fit a sparse “ma
Page 37 and 38: Two SNP race classifierafd0860639
Page 39 and 40: Independent validation of resultsTh
Page 41 and 42: CodeR Library available for downloa

Fitting Statistical models to data with many more variables than ...

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