1. Chapter 12
Application of Gibbs Sampling in Variance Component
Estimation and Prediction of Breeding Value
Linear Models for Prediction of Animal Breeding Values
R .A. Mrode
Gota Morota
May 6, 2010
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2. Outline
Pre Gibbs Sampling Era
Gibbs Sampling
Gibbs Sampling vs. REML and BLUP
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3. Outline
Pre Gibbs Sampling Era
Gibbs Sampling
Gibbs Sampling vs. REML and BLUP
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4. Outline
Pre Gibbs Sampling Era
Gibbs Sampling
Gibbs Sampling vs. REML and BLUP
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5. Controversy over REML
• gives joint modes of variance components rather than
marginal modes
quadratic loss function =
ˆ
(yij − yij )2
• not all variance components have equal importance
⇓
variance components of no interest (nuisance paramters)
should be integrated out
⇓
only paramters of interest should be maximized in the
likelihood
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6. VEIL (1990)
Variance Estimation from Integrated Likelihood
Gianola D, Foulley JL (1990) Variance Estimation from Integrated
Likelihood (VEIL). Genet Sel Evol 22, 403-417
Inference is based on the marginal posterior distribtuion of each of
the variance components.
⇓
Approximations to the marginal distributions were proposed.
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7. Gibbs Sampling
Conditional distribution
⇓
Marginal distribution
• difficult
p (θa , θb , θc , θd )d θb d θc d θd
• easy
sample from p (θa |θb , θc , θd )
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8. History of Bayesian Analyses Coupled with Gibbs
Sampling
• Gelfand AE, and Smith AFM (1990): Introduced in statistics
• Gelfand et al.(1990): One-way radom effects model
• CS.Wang et al. (1993): Univariate mixed linear model using
simulated data
• CS.Wang et al. (1994): Univariate mixed linear model using
field data (litter size)
• DA Sorensen et al. (1995) : Threshold models
• Jamorozik and Shaeffer (1997): Random Regression
Gibbs Sampling
It has been applied to a wide range of animal breeding problems.
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9. Likelihood
Bayes’ theorem:
joint posterior distribution ∝ likelihood × priors
• Consider the linear model
y = XB + Zu + e
• Further,
e|σ2 ∼ N (0, Iσ2 )
e
e
• The conditinal distribution which generates the data
(likelihood):
y|B, u, σ2 ∼ N (XB + Zu, Iσ2 )
e
e
n
∝ (σ2 )− 2 exp −
e
(y − XB − Zu) (y − XB − Zu)
2σ2
e
(1)
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10. Prior Distribution for Location Parameters
• Prior for B:
p (B) ∝ constant
(2)
• Prior for u
u|Aσ2 ∼ N (0, Aσ2 )
u
u
q
∝ (σ2 )− 2 exp −
u
q
∝ (σ2 )− 2 exp −
u
(u − 0) (u − 0)
2Aσ2
u
u A−1 u
2σ2
u
(3)
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11. Prior Distribution for Scale Parameters
• Prior for σ2
u
2
p (σ2 |su , υu ) ∝ (σ2 )−
u
u
υu +2
2
exp −
2
υu su
2σ2
u
(4)
exp −
2
υe se
2 σ2
e
(5)
• Prior for σ2
e
2
p (σ2 |se , υe ) ∝ (σ2 )−
e
e
υe +2
2
Scaled inverted χ2 distribution
Commoly used for priors of variance components in the Bayesian
analyses
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12. Joint Posterior Distribution
Multiplication of likelihood (1) and priors (4) to (5)
2
2
Joint posterior distribution = p (B, u, σ2 , σ2 |y, su , υu , se , υe )
u
e
2
2
∝ p (y|B, u, σ2 ) p (B) p (u|σ2 ) p (σ2 |su , υu ) p (σ2 |se , υe )
e
u
u
e
∝ (σ2 )−
u
(σ2 )−
e
n+υe +2
2
exp −
q+υu +2
2
exp −
2
u A−1 u + υu su
2σ2
u
2
(y − XB − Zu) (y − XB − Zu) + υe se
2σ2
e
(6)
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13. Fully Conditional Distribution for Location Parameters
The fully conditional distribution of each parameter is obtained by
regarding all other parameters in (6) as known.
• B:
p (B|u, σ2 , σ2 , y) ∝ exp −
e
u
(y − XB − Zu) (y − XB − Zu)
2σ2
e
B|u, σ2 , σ2 , y ∼ N ((X X)−1 X (y − Zu), (X X)−1 σ2 )
e
e
u
(7)



2 −1




2
−1 σe 
Z Zi + A
 σ 

 i



e

i
σ2 i
u
(8)
• u:
ui |B, u−i , σ2 , σ2 , y
u
e



˜
∼ N ui ,


where


q
2 −1



Z Zi + A−1 σe  Z (y − XB −

 i
˜
Zj uj )
ui =  i


i
σ2 i
u
j=1,j i
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14. Fully Conditional Distribution for Scale Parameters
The fully conditional distribution of each parameter is obtained by
regarding all other parameters in (6) as known.
• σ2 :
e
p (σ2 |B, u, σ2 , y) ∝
e
u
(σ2 )−
e
n+υe +2
2
exp −
2
(y − XB − Zu) (y − XB − Zu) + υe se
2σ2
e
(9)
2
˜2
υe = n + υe , se = [(y − XB − Zu) (y − XB − Zu) + υe se ]/υe
˜
˜
• σ2 :
u
p (σ2 |B, u, σ2 , y) ∝ (σ2 )−
u
e
u
q+υu +2
2
exp −
2
u A−1 u + υu su
2 σ2
u
(10)
2
˜2
υu = q + υu , su = [u A−1 u + υu su ]/υu
˜
˜
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15. Sampling
• Consider following linear model
XT X
XT Z
B
XT y
= T
ZT X ZT Z + A−1 α u
Z y
LHS · C = RHS
• Iteration
Ci |(ELSE) ∼ N
RHS[i] −
j =1,j i
LHS[i, j] · Bj
LHS[i, i]
,
σ2
e
LHS[i, i]
2
2
σ2 |(ELSE) ∼ [(y − Xb − Zu)T (y − Xb − Zu) + υe se ] · χ−+υe
e
n
2
2
σ2 |(ELSE) ∼ [uT A−1 u + υu su ] · χ−+υu
a
q
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16. Inferences from the Gibbs Sampling Output
σ2 : σ21 , σ21 , · · · , σ2k ,
u
u
u
u
• Direct inference from samples
post mean =
post variance =
k
i =1
σ2i
u
k
k
2
i =1 (σui
− post mean)2
k
• Density Estimation
• Kernel Density Estimation
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17. Gibbs Sampling vs. REML and BLUP
In practice, we don’t know the variance components.
REML → BLUP procedure
• does not take into account uncertainty in estimating variance
components
• estimating variance components are ignored in predicting
breeding values
• BLUP from the MME is no longer BLUP (empirical BLUP)
Gibbs Sampling
Able to estimates location paramters and scale paratmers jointly.
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18. Summary
Bayesian is great!
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