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1. 1. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling methods for Bayesian discrimination between embedded models Christian P. Robert Universit´ Paris Dauphine & CREST-INSEE e http://www.ceremade.dauphine.fr/~xian 45th Scientiﬁc Meeting of the Italian Statistical Society Padova, 16 giugno 2010 Joint work with J.-M. Marin
2. 2. Importance sampling methods for Bayesian discrimination between embedded models Outline 1 Bayesian model choice 2 Importance sampling model comparison solutions compared
3. 3. Importance sampling methods for Bayesian discrimination between embedded models Bayesian model choice Model choice Model choice as model comparison Choice between models Several models available for the same observation Mi : x ∼ fi (x|θi ), i∈I Subsitute hypotheses with models
4. 4. Importance sampling methods for Bayesian discrimination between embedded models Bayesian model choice Model choice Model choice as model comparison Choice between models Several models available for the same observation Mi : x ∼ fi (x|θi ), i∈I Subsitute hypotheses with models
5. 5. Importance sampling methods for Bayesian discrimination between embedded models Bayesian model choice Model choice Bayesian model choice Probabilise the entire model/parameter space allocate probabilities pi to all models Mi deﬁne priors πi (θi ) for each parameter space Θi compute pi fi (x|θi )πi (θi )dθi Θi π(Mi |x) = pj fj (x|θj )πj (θj )dθj j Θj take largest π(Mi |x) to determine “best” model,
6. 6. Importance sampling methods for Bayesian discrimination between embedded models Bayesian model choice Model choice Bayesian model choice Probabilise the entire model/parameter space allocate probabilities pi to all models Mi deﬁne priors πi (θi ) for each parameter space Θi compute pi fi (x|θi )πi (θi )dθi Θi π(Mi |x) = pj fj (x|θj )πj (θj )dθj j Θj take largest π(Mi |x) to determine “best” model,
7. 7. Importance sampling methods for Bayesian discrimination between embedded models Bayesian model choice Model choice Bayesian model choice Probabilise the entire model/parameter space allocate probabilities pi to all models Mi deﬁne priors πi (θi ) for each parameter space Θi compute pi fi (x|θi )πi (θi )dθi Θi π(Mi |x) = pj fj (x|θj )πj (θj )dθj j Θj take largest π(Mi |x) to determine “best” model,
8. 8. Importance sampling methods for Bayesian discrimination between embedded models Bayesian model choice Model choice Bayesian model choice Probabilise the entire model/parameter space allocate probabilities pi to all models Mi deﬁne priors πi (θi ) for each parameter space Θi compute pi fi (x|θi )πi (θi )dθi Θi π(Mi |x) = pj fj (x|θj )πj (θj )dθj j Θj take largest π(Mi |x) to determine “best” model,
9. 9. Importance sampling methods for Bayesian discrimination between embedded models Bayesian model choice Bayes factor Bayes factor Deﬁnition (Bayes factors) For comparing model M0 with θ ∈ Θ0 vs. M1 with θ ∈ Θ1 , under priors π0 (θ) and π1 (θ), central quantity f0 (x|θ0 )π0 (θ0 )dθ0 π(Θ0 |x) π(Θ0 ) Θ0 B01 = = π(Θ1 |x) π(Θ1 ) f1 (x|θ)π1 (θ1 )dθ1 Θ1 [Jeﬀreys, 1939]
10. 10. Importance sampling methods for Bayesian discrimination between embedded models Bayesian model choice Evidence Evidence Problems using a similar quantity, the evidence Zk = πk (θk )Lk (θk ) dθk , Θk aka the marginal likelihood. [Jeﬀreys, 1939]
11. 11. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared A comparison of importance sampling solutions 1 Bayesian model choice 2 Importance sampling model comparison solutions compared Regular importance Bridge sampling Mixtures to bridge Harmonic means Chib’s solution The Savage–Dickey ratio [Marin & Robert, 2010]
12. 12. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Regular importance Bayes factor approximation When approximating the Bayes factor f0 (x|θ0 )π0 (θ0 )dθ0 Θ0 B01 = f1 (x|θ1 )π1 (θ1 )dθ1 Θ1 use of importance functions 0 and 1 and n−1 0 n0 i i i=1 f0 (x|θ0 )π0 (θ0 )/ i 0 (θ0 ) B01 = n−1 1 n1 i i i=1 f1 (x|θ1 )π1 (θ1 )/ i 1 (θ1 )
13. 13. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Regular importance Diabetes in Pima Indian women Example (R benchmark) “A population of women who were at least 21 years old, of Pima Indian heritage and living near Phoenix (AZ), was tested for diabetes according to WHO criteria. The data were collected by the US National Institute of Diabetes and Digestive and Kidney Diseases. 200 Pima Indian women with observed variables plasma glucose concentration in oral glucose tolerance test diastolic blood pressure diabetes pedigree function presence/absence of diabetes
14. 14. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Regular importance Probit modelling on Pima Indian women Probability of diabetes function of above variables P(y = 1|x) = Φ(x1 β1 + x2 β2 + x3 β3 ) , Test of H0 : β3 = 0 for 200 observations of Pima.tr based on a g-prior modelling: β ∼ N3 (0, n XT X)−1
15. 15. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Regular importance Probit modelling on Pima Indian women Probability of diabetes function of above variables P(y = 1|x) = Φ(x1 β1 + x2 β2 + x3 β3 ) , Test of H0 : β3 = 0 for 200 observations of Pima.tr based on a g-prior modelling: β ∼ N3 (0, n XT X)−1
16. 16. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Regular importance Importance sampling for the Pima Indian dataset Use of the importance function inspired from the MLE estimate distribution ˆ ˆ β ∼ N (β, Σ) R Importance sampling code model1=summary(glm(y~-1+X1,family=binomial(link=probit))) is1=rmvnorm(Niter,mean=model1\$coeff[,1],sigma=2*model1\$cov.unscaled) is2=rmvnorm(Niter,mean=model2\$coeff[,1],sigma=2*model2\$cov.unscaled) bfis=mean(exp(probitlpost(is1,y,X1)-dmvlnorm(is1,mean=model1\$coeff[,1], sigma=2*model1\$cov.unscaled))) / mean(exp(probitlpost(is2,y,X2)- dmvlnorm(is2,mean=model2\$coeff[,1],sigma=2*model2\$cov.unscaled)))
17. 17. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Regular importance Importance sampling for the Pima Indian dataset Use of the importance function inspired from the MLE estimate distribution ˆ ˆ β ∼ N (β, Σ) R Importance sampling code model1=summary(glm(y~-1+X1,family=binomial(link=probit))) is1=rmvnorm(Niter,mean=model1\$coeff[,1],sigma=2*model1\$cov.unscaled) is2=rmvnorm(Niter,mean=model2\$coeff[,1],sigma=2*model2\$cov.unscaled) bfis=mean(exp(probitlpost(is1,y,X1)-dmvlnorm(is1,mean=model1\$coeff[,1], sigma=2*model1\$cov.unscaled))) / mean(exp(probitlpost(is2,y,X2)- dmvlnorm(is2,mean=model2\$coeff[,1],sigma=2*model2\$cov.unscaled)))
18. 18. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Regular importance Diabetes in Pima Indian women Comparison of the variation of the Bayes factor approximations based on 100 replicas for 20, 000 simulations from the prior and the above MLE importance sampler 5 4 q 3 2 Monte Carlo Importance sampling
19. 19. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Bridge sampling Bridge sampling Special case: If π1 (θ1 |x) ∝ π1 (θ1 |x) ˜ π2 (θ2 |x) ∝ π2 (θ2 |x) ˜ live on the same space (Θ1 = Θ2 ), then n 1 π1 (θi |x) ˜ B12 ≈ θi ∼ π2 (θ|x) n π2 (θi |x) ˜ i=1 [Gelman & Meng, 1998; Chen, Shao & Ibrahim, 2000]
20. 20. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Bridge sampling (Further) bridge sampling General identity: π2 (θ|x)α(θ)π1 (θ|x)dθ ˜ B12 = ∀ α(·) π1 (θ|x)α(θ)π2 (θ|x)dθ ˜ n1 1 π2 (θ1i |x)α(θ1i ) ˜ n1 i=1 ≈ n2 θji ∼ πj (θ|x) 1 π1 (θ2i |x)α(θ2i ) ˜ n2 i=1
21. 21. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Bridge sampling Optimal bridge sampling The optimal choice of auxiliary function is n1 + n2 α = n1 π1 (θ|x) + n2 π2 (θ|x) leading to n1 1 π2 (θ1i |x) ˜ n1 n1 π1 (θ1i |x) + n2 π2 (θ1i |x) i=1 B12 ≈ n2 1 π1 (θ2i |x) ˜ n2 n1 π1 (θ2i |x) + n2 π2 (θ2i |x) i=1 Back later! Drawback: Dependence on the unknown normalising constants solved iteratively
22. 22. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Bridge sampling Optimal bridge sampling The optimal choice of auxiliary function is n1 + n2 α = n1 π1 (θ|x) + n2 π2 (θ|x) leading to n1 1 π2 (θ1i |x) ˜ n1 n1 π1 (θ1i |x) + n2 π2 (θ1i |x) i=1 B12 ≈ n2 1 π1 (θ2i |x) ˜ n2 n1 π1 (θ2i |x) + n2 π2 (θ2i |x) i=1 Back later! Drawback: Dependence on the unknown normalising constants solved iteratively
23. 23. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Bridge sampling Extension to varying dimensions When dim(Θ1 ) = dim(Θ2 ), e.g. θ2 = (θ1 , ψ), introduction of a pseudo-posterior density, ω(ψ|θ1 , x), augmenting π1 (θ1 |x) into joint distribution π1 (θ1 |x) × ω(ψ|θ1 , x) on Θ2 so that π1 (θ1 |x)α(θ1 , ψ)π2 (θ1 , ψ|x)dθ1 ω(ψ|θ1 , x) dψ ˜ B12 = π2 (θ1 , ψ|x)α(θ1 , ψ)π1 (θ1 |x)dθ1 ω(ψ|θ1 , x) dψ ˜ π1 (θ1 )ω(ψ|θ1 ) ˜ Eϕ [˜1 (θ1 )ω(ψ|θ1 )/ϕ(θ1 , ψ)] π = Eπ2 = π2 (θ1 , ψ) ˜ Eϕ [˜2 (θ1 , ψ)/ϕ(θ1 , ψ)] π for any conditional density ω(ψ|θ1 ) and any joint density ϕ.
24. 24. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Bridge sampling Extension to varying dimensions When dim(Θ1 ) = dim(Θ2 ), e.g. θ2 = (θ1 , ψ), introduction of a pseudo-posterior density, ω(ψ|θ1 , x), augmenting π1 (θ1 |x) into joint distribution π1 (θ1 |x) × ω(ψ|θ1 , x) on Θ2 so that π1 (θ1 |x)α(θ1 , ψ)π2 (θ1 , ψ|x)dθ1 ω(ψ|θ1 , x) dψ ˜ B12 = π2 (θ1 , ψ|x)α(θ1 , ψ)π1 (θ1 |x)dθ1 ω(ψ|θ1 , x) dψ ˜ π1 (θ1 )ω(ψ|θ1 ) ˜ Eϕ [˜1 (θ1 )ω(ψ|θ1 )/ϕ(θ1 , ψ)] π = Eπ2 = π2 (θ1 , ψ) ˜ Eϕ [˜2 (θ1 , ψ)/ϕ(θ1 , ψ)] π for any conditional density ω(ψ|θ1 ) and any joint density ϕ.
25. 25. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Bridge sampling Illustration for the Pima Indian dataset Use of the MLE induced conditional of β3 given (β1 , β2 ) as a pseudo-posterior and mixture of both MLE approximations on β3 in bridge sampling estimate R bridge sampling code cova=model2\$cov.unscaled expecta=model2\$coeff[,1] covw=cova[3,3]-t(cova[1:2,3])%*%ginv(cova[1:2,1:2])%*%cova[1:2,3] probit1=hmprobit(Niter,y,X1) probit2=hmprobit(Niter,y,X2) pseudo=rnorm(Niter,meanw(probit1),sqrt(covw)) probit1p=cbind(probit1,pseudo) bfbs=mean(exp(probitlpost(probit2[,1:2],y,X1)+dnorm(probit2[,3],meanw(probit2[,1:2]), sqrt(covw),log=T))/ (dmvnorm(probit2,expecta,cova)+dnorm(probit2[,3],expecta[3], cova[3,3])))/ mean(exp(probitlpost(probit1p,y,X2))/(dmvnorm(probit1p,expecta,cova)+ dnorm(pseudo,expecta[3],cova[3,3])))
26. 26. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Bridge sampling Illustration for the Pima Indian dataset Use of the MLE induced conditional of β3 given (β1 , β2 ) as a pseudo-posterior and mixture of both MLE approximations on β3 in bridge sampling estimate R bridge sampling code cova=model2\$cov.unscaled expecta=model2\$coeff[,1] covw=cova[3,3]-t(cova[1:2,3])%*%ginv(cova[1:2,1:2])%*%cova[1:2,3] probit1=hmprobit(Niter,y,X1) probit2=hmprobit(Niter,y,X2) pseudo=rnorm(Niter,meanw(probit1),sqrt(covw)) probit1p=cbind(probit1,pseudo) bfbs=mean(exp(probitlpost(probit2[,1:2],y,X1)+dnorm(probit2[,3],meanw(probit2[,1:2]), sqrt(covw),log=T))/ (dmvnorm(probit2,expecta,cova)+dnorm(probit2[,3],expecta[3], cova[3,3])))/ mean(exp(probitlpost(probit1p,y,X2))/(dmvnorm(probit1p,expecta,cova)+ dnorm(pseudo,expecta[3],cova[3,3])))
27. 27. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Bridge sampling Diabetes in Pima Indian women (cont’d) Comparison of the variation of the Bayes factor approximations based on 100 × 20, 000 simulations from the prior (MC), the above bridge sampler and the above importance sampler 5 4 q q 3 q 2 MC Bridge IS
28. 28. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Mixtures to bridge Approximating Zk using a mixture representation Bridge sampling redux Design a speciﬁc mixture for simulation [importance sampling] purposes, with density ϕk (θk ) ∝ ω1 πk (θk )Lk (θk ) + ϕ(θk ) , where ϕ(·) is arbitrary (but normalised) Note: ω1 is not a probability weight [Chopin & Robert, 2010]
29. 29. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Mixtures to bridge Approximating Zk using a mixture representation Bridge sampling redux Design a speciﬁc mixture for simulation [importance sampling] purposes, with density ϕk (θk ) ∝ ω1 πk (θk )Lk (θk ) + ϕ(θk ) , where ϕ(·) is arbitrary (but normalised) Note: ω1 is not a probability weight [Chopin & Robert, 2010]
30. 30. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Mixtures to bridge Approximating Z using a mixture representation (cont’d) Corresponding MCMC (=Gibbs) sampler At iteration t 1 Take δ (t) = 1 with probability (t−1) (t−1) (t−1) (t−1) (t−1) ω1 πk (θk )Lk (θk ) ω1 πk (θk )Lk (θk ) + ϕ(θk ) and δ (t) = 2 otherwise; (t) (t−1) 2 If δ (t) = 1, generate θk ∼ MCMC(θk , θk ) where MCMC(θk , θk ) denotes an arbitrary MCMC kernel associated with the posterior πk (θk |x) ∝ πk (θk )Lk (θk ); (t) 3 If δ (t) = 2, generate θk ∼ ϕ(θk ) independently
31. 31. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Mixtures to bridge Approximating Z using a mixture representation (cont’d) Corresponding MCMC (=Gibbs) sampler At iteration t 1 Take δ (t) = 1 with probability (t−1) (t−1) (t−1) (t−1) (t−1) ω1 πk (θk )Lk (θk ) ω1 πk (θk )Lk (θk ) + ϕ(θk ) and δ (t) = 2 otherwise; (t) (t−1) 2 If δ (t) = 1, generate θk ∼ MCMC(θk , θk ) where MCMC(θk , θk ) denotes an arbitrary MCMC kernel associated with the posterior πk (θk |x) ∝ πk (θk )Lk (θk ); (t) 3 If δ (t) = 2, generate θk ∼ ϕ(θk ) independently
32. 32. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Mixtures to bridge Approximating Z using a mixture representation (cont’d) Corresponding MCMC (=Gibbs) sampler At iteration t 1 Take δ (t) = 1 with probability (t−1) (t−1) (t−1) (t−1) (t−1) ω1 πk (θk )Lk (θk ) ω1 πk (θk )Lk (θk ) + ϕ(θk ) and δ (t) = 2 otherwise; (t) (t−1) 2 If δ (t) = 1, generate θk ∼ MCMC(θk , θk ) where MCMC(θk , θk ) denotes an arbitrary MCMC kernel associated with the posterior πk (θk |x) ∝ πk (θk )Lk (θk ); (t) 3 If δ (t) = 2, generate θk ∼ ϕ(θk ) independently
33. 33. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Mixtures to bridge Evidence approximation by mixtures Rao-Blackwellised estimate T ˆ 1 ξ= (t) ω1 πk (θk )Lk (θk ) (t) (t) (t) ω1 πk (θk )Lk (θk ) + ϕ(θk ) , (t) T t=1 converges to ω1 Zk /{ω1 Zk + 1} 3k ˆ ˆ ˆ Deduce Zˆ from ω1 Z3k /{ω1 Z3k + 1} = ξ ie T (t) (t) (t) (t) (t) t=1 ω1 πk (θk )Lk (θk ) ω1 π(θk )Lk (θk ) + ϕ(θk ) ˆ Z3k = T (t) (t) (t) (t) t=1 ϕ(θk ) ω1 πk (θk )Lk (θk ) + ϕ(θk ) [Bridge sampler]
34. 34. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Mixtures to bridge Evidence approximation by mixtures Rao-Blackwellised estimate T ˆ 1 ξ= (t) ω1 πk (θk )Lk (θk ) (t) (t) (t) ω1 πk (θk )Lk (θk ) + ϕ(θk ) , (t) T t=1 converges to ω1 Zk /{ω1 Zk + 1} 3k ˆ ˆ ˆ Deduce Zˆ from ω1 Z3k /{ω1 Z3k + 1} = ξ ie T (t) (t) (t) (t) (t) t=1 ω1 πk (θk )Lk (θk ) ω1 π(θk )Lk (θk ) + ϕ(θk ) ˆ Z3k = T (t) (t) (t) (t) t=1 ϕ(θk ) ω1 πk (θk )Lk (θk ) + ϕ(θk ) [Bridge sampler]
35. 35. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Harmonic means The original harmonic mean estimator When θki ∼ πk (θ|x), T 1 1 T L(θkt |x) t=1 is an unbiased estimator of 1/mk (x) [Newton & Raftery, 1994] Highly dangerous: Most often leads to an inﬁnite variance!!!
36. 36. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Harmonic means The original harmonic mean estimator When θki ∼ πk (θ|x), T 1 1 T L(θkt |x) t=1 is an unbiased estimator of 1/mk (x) [Newton & Raftery, 1994] Highly dangerous: Most often leads to an inﬁnite variance!!!
37. 37. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Harmonic means Approximating Zk from a posterior sample Use of the [harmonic mean] identity ϕ(θk ) ϕ(θk ) πk (θk )Lk (θk ) 1 Eπk x = dθk = πk (θk )Lk (θk ) πk (θk )Lk (θk ) Zk Zk no matter what the proposal ϕ(·) is. [Gelfand & Dey, 1994; Bartolucci et al., 2006] Direct exploitation of the MCMC output
38. 38. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Harmonic means Approximating Zk from a posterior sample Use of the [harmonic mean] identity ϕ(θk ) ϕ(θk ) πk (θk )Lk (θk ) 1 Eπk x = dθk = πk (θk )Lk (θk ) πk (θk )Lk (θk ) Zk Zk no matter what the proposal ϕ(·) is. [Gelfand & Dey, 1994; Bartolucci et al., 2006] Direct exploitation of the MCMC output
39. 39. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Harmonic means Comparison with regular importance sampling Harmonic mean: Constraint opposed to usual importance sampling constraints: ϕ(θ) must have lighter (rather than fatter) tails than πk (θk )Lk (θk ) for the approximation T (t) 1 ϕ(θk ) Z1k = 1 (t) (t) T πk (θk )Lk (θk ) t=1 to have a ﬁnite variance. E.g., use ﬁnite support kernels for ϕ
40. 40. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Harmonic means Comparison with regular importance sampling Harmonic mean: Constraint opposed to usual importance sampling constraints: ϕ(θ) must have lighter (rather than fatter) tails than πk (θk )Lk (θk ) for the approximation T (t) 1 ϕ(θk ) Z1k = 1 (t) (t) T πk (θk )Lk (θk ) t=1 to have a ﬁnite variance. E.g., use ﬁnite support kernels for ϕ
41. 41. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Harmonic means Comparison with regular importance sampling (cont’d) Compare Z1k with a standard importance sampling approximation T (t) (t) 1 πk (θk )Lk (θk ) Z2k = (t) T ϕ(θk ) t=1 (t) where the θk ’s are generated from the density ϕ(·) (with fatter tails like t’s)
42. 42. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Harmonic means HPD indicator as ϕ Use the convex hull of MCMC simulations corresponding to the 10% HPD region (easily derived!) and ϕ as indicator: 10 ϕ(θ) = Id(θ,θ(t) )≤ T t∈HPD
43. 43. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Harmonic means Diabetes in Pima Indian women (cont’d) Comparison of the variation of the Bayes factor approximations based on 100 replicas for 20, 000 simulations for a simulation from the above harmonic mean sampler and importance samplers 3.102 3.104 3.106 3.108 3.110 3.112 3.114 3.116 q q Harmonic mean Importance sampling
44. 44. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Chib’s representation Direct application of Bayes’ theorem: given x ∼ fk (x|θk ) and θk ∼ πk (θk ), fk (x|θk ) πk (θk ) Zk = mk (x) = πk (θk |x) Use of an approximation to the posterior ∗ ∗ fk (x|θk ) πk (θk ) Zk = mk (x) = . ˆ ∗ πk (θk |x)
45. 45. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Chib’s representation Direct application of Bayes’ theorem: given x ∼ fk (x|θk ) and θk ∼ πk (θk ), fk (x|θk ) πk (θk ) Zk = mk (x) = πk (θk |x) Use of an approximation to the posterior ∗ ∗ fk (x|θk ) πk (θk ) Zk = mk (x) = . ˆ ∗ πk (θk |x)
46. 46. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Case of latent variables For missing variable z as in mixture models, natural Rao-Blackwell estimate T ∗ 1 ∗ (t) πk (θk |x) = πk (θk |x, zk ) , T t=1 (t) where the zk ’s are Gibbs sampled latent variables Skip diﬃculties...
47. 47. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Label switching A mixture model [special case of missing variable model] is invariant under permutations of the indices of the components. E.g., mixtures 0.3N (0, 1) + 0.7N (2.3, 1) and 0.7N (2.3, 1) + 0.3N (0, 1) are exactly the same! c The component parameters θi are not identiﬁable marginally since they are exchangeable
48. 48. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Label switching A mixture model [special case of missing variable model] is invariant under permutations of the indices of the components. E.g., mixtures 0.3N (0, 1) + 0.7N (2.3, 1) and 0.7N (2.3, 1) + 0.3N (0, 1) are exactly the same! c The component parameters θi are not identiﬁable marginally since they are exchangeable
49. 49. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Connected diﬃculties 1 Number of modes of the likelihood of order O(k!): c Maximization and even [MCMC] exploration of the posterior surface harder 2 Under exchangeable priors on (θ, p) [prior invariant under permutation of the indices], all posterior marginals are identical: c Posterior expectation of θ1 equal to posterior expectation of θ2
50. 50. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Connected diﬃculties 1 Number of modes of the likelihood of order O(k!): c Maximization and even [MCMC] exploration of the posterior surface harder 2 Under exchangeable priors on (θ, p) [prior invariant under permutation of the indices], all posterior marginals are identical: c Posterior expectation of θ1 equal to posterior expectation of θ2
51. 51. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution License Since Gibbs output does not produce exchangeability, the Gibbs sampler has not explored the whole parameter space: it lacks energy to switch simultaneously enough component allocations at once 0.2 0.3 0.4 0.5 −1 0 1 2 3 µi 0 100 200 n 300 400 500 pi −1 0 µ 1 i 2 3 0.4 0.6 0.8 1.0 0.2 0.3 0.4 0.5 σi pi 0 100 200 300 400 500 0.2 0.3 0.4 0.5 n pi 0.4 0.6 0.8 1.0 −1 0 1 2 3 σi µi 0 100 200 300 400 500 0.4 0.6 0.8 1.0 n σi
52. 52. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Label switching paradox We should observe the exchangeability of the components [label switching] to conclude about convergence of the Gibbs sampler. If we observe it, then we do not know how to estimate the parameters. If we do not, then we are uncertain about the convergence!!!
53. 53. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Label switching paradox We should observe the exchangeability of the components [label switching] to conclude about convergence of the Gibbs sampler. If we observe it, then we do not know how to estimate the parameters. If we do not, then we are uncertain about the convergence!!!
54. 54. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Label switching paradox We should observe the exchangeability of the components [label switching] to conclude about convergence of the Gibbs sampler. If we observe it, then we do not know how to estimate the parameters. If we do not, then we are uncertain about the convergence!!!
55. 55. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Compensation for label switching (t) For mixture models, zk usually fails to visit all conﬁgurations in a balanced way, despite the symmetry predicted by the theory 1 πk (θk |x) = πk (σ(θk )|x) = πk (σ(θk )|x) k! σ∈S for all σ’s in Sk , set of all permutations of {1, . . . , k}. Consequences on numerical approximation, biased by an order k! Recover the theoretical symmetry by using T ∗ 1 ∗ (t) πk (θk |x) = πk (σ(θk )|x, zk ) . T k! σ∈Sk t=1 [Berkhof, Mechelen, & Gelman, 2003]
56. 56. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Compensation for label switching (t) For mixture models, zk usually fails to visit all conﬁgurations in a balanced way, despite the symmetry predicted by the theory 1 πk (θk |x) = πk (σ(θk )|x) = πk (σ(θk )|x) k! σ∈S for all σ’s in Sk , set of all permutations of {1, . . . , k}. Consequences on numerical approximation, biased by an order k! Recover the theoretical symmetry by using T ∗ 1 ∗ (t) πk (θk |x) = πk (σ(θk )|x, zk ) . T k! σ∈Sk t=1 [Berkhof, Mechelen, & Gelman, 2003]
57. 57. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Galaxy dataset n = 82 galaxies as a mixture of k normal distributions with both mean and variance unknown. [Roeder, 1992] Average density 0.8 0.6 Relative Frequency 0.4 0.2 0.0 −2 −1 0 1 2 3 data
58. 58. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Galaxy dataset (k) ∗ Using only the original estimate, with θk as the MAP estimator, log(mk (x)) = −105.1396 ˆ for k = 3 (based on 103 simulations), while introducing the permutations leads to log(mk (x)) = −103.3479 ˆ Note that −105.1396 + log(3!) = −103.3479 k 2 3 4 5 6 7 8 mk (x) -115.68 -103.35 -102.66 -101.93 -102.88 -105.48 -108.44 Estimations of the marginal likelihoods by the symmetrised Chib’s approximation (based on 105 Gibbs iterations and, for k > 5, 100 permutations selected at random in Sk ). [Lee, Marin, Mengersen & Robert, 2008]
59. 59. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Galaxy dataset (k) ∗ Using only the original estimate, with θk as the MAP estimator, log(mk (x)) = −105.1396 ˆ for k = 3 (based on 103 simulations), while introducing the permutations leads to log(mk (x)) = −103.3479 ˆ Note that −105.1396 + log(3!) = −103.3479 k 2 3 4 5 6 7 8 mk (x) -115.68 -103.35 -102.66 -101.93 -102.88 -105.48 -108.44 Estimations of the marginal likelihoods by the symmetrised Chib’s approximation (based on 105 Gibbs iterations and, for k > 5, 100 permutations selected at random in Sk ). [Lee, Marin, Mengersen & Robert, 2008]
60. 60. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Galaxy dataset (k) ∗ Using only the original estimate, with θk as the MAP estimator, log(mk (x)) = −105.1396 ˆ for k = 3 (based on 103 simulations), while introducing the permutations leads to log(mk (x)) = −103.3479 ˆ Note that −105.1396 + log(3!) = −103.3479 k 2 3 4 5 6 7 8 mk (x) -115.68 -103.35 -102.66 -101.93 -102.88 -105.48 -108.44 Estimations of the marginal likelihoods by the symmetrised Chib’s approximation (based on 105 Gibbs iterations and, for k > 5, 100 permutations selected at random in Sk ). [Lee, Marin, Mengersen & Robert, 2008]
61. 61. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Case of the probit model For the completion by z, 1 π (θ|x) = ˆ π(θ|x, z (t) ) T t is a simple average of normal densities R Bridge sampling code gibbs1=gibbsprobit(Niter,y,X1) gibbs2=gibbsprobit(Niter,y,X2) bfchi=mean(exp(dmvlnorm(t(t(gibbs2\$mu)-model2\$coeff[,1]),mean=rep(0,3), sigma=gibbs2\$Sigma2)-probitlpost(model2\$coeff[,1],y,X2)))/ mean(exp(dmvlnorm(t(t(gibbs1\$mu)-model1\$coeff[,1]),mean=rep(0,2), sigma=gibbs1\$Sigma2)-probitlpost(model1\$coeff[,1],y,X1)))
62. 62. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Case of the probit model For the completion by z, 1 π (θ|x) = ˆ π(θ|x, z (t) ) T t is a simple average of normal densities R Bridge sampling code gibbs1=gibbsprobit(Niter,y,X1) gibbs2=gibbsprobit(Niter,y,X2) bfchi=mean(exp(dmvlnorm(t(t(gibbs2\$mu)-model2\$coeff[,1]),mean=rep(0,3), sigma=gibbs2\$Sigma2)-probitlpost(model2\$coeff[,1],y,X2)))/ mean(exp(dmvlnorm(t(t(gibbs1\$mu)-model1\$coeff[,1]),mean=rep(0,2), sigma=gibbs1\$Sigma2)-probitlpost(model1\$coeff[,1],y,X1)))
63. 63. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared Chib’s solution Diabetes in Pima Indian women (cont’d) Comparison of the variation of the Bayes factor approximations based on 100 replicas for 20, 000 simulations for a simulation from the above Chib’s and importance samplers q 0.0255 q q q q 0.0250 0.0245 0.0240 q q Chib's method importance sampling
64. 64. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio The Savage–Dickey ratio Special representation of the Bayes factor used for simulation Original version (Dickey, AoMS, 1971)
65. 65. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Savage’s density ratio theorem Given a test H0 : θ = θ0 in a model f (x|θ, ψ) with a nuisance parameter ψ, under priors π0 (ψ) and π1 (θ, ψ) such that π1 (ψ|θ0 ) = π0 (ψ) then π1 (θ0 |x) B01 = , π1 (θ0 ) with the obvious notations π1 (θ) = π1 (θ, ψ)dψ , π1 (θ|x) = π1 (θ, ψ|x)dψ , [Dickey, 1971; Verdinelli & Wasserman, 1995]
66. 66. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Measure-theoretic diﬃculty Representation depends on the choice of versions of conditional densities: π0 (ψ)f (x|θ0 , ψ) dψ B01 = [by deﬁnition] π1 (θ, ψ)f (x|θ, ψ) dψdθ π1 (ψ|θ0 )f (x|θ0 , ψ) dψ π1 (θ0 ) = [speciﬁc version of π1 (ψ|θ0 ) π1 (θ, ψ)f (x|θ, ψ) dψdθ π1 (θ0 ) and arbitrary version of π1 (θ0 )] π1 (θ0 , ψ)f (x|θ0 , ψ) dψ = [speciﬁc version of π1 (θ0 , ψ)] m1 (x)π1 (θ0 ) π1 (θ0 |x) = [version dependent] π1 (θ0 )
67. 67. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Measure-theoretic diﬃculty Representation depends on the choice of versions of conditional densities: π0 (ψ)f (x|θ0 , ψ) dψ B01 = [by deﬁnition] π1 (θ, ψ)f (x|θ, ψ) dψdθ π1 (ψ|θ0 )f (x|θ0 , ψ) dψ π1 (θ0 ) = [speciﬁc version of π1 (ψ|θ0 ) π1 (θ, ψ)f (x|θ, ψ) dψdθ π1 (θ0 ) and arbitrary version of π1 (θ0 )] π1 (θ0 , ψ)f (x|θ0 , ψ) dψ = [speciﬁc version of π1 (θ0 , ψ)] m1 (x)π1 (θ0 ) π1 (θ0 |x) = [version dependent] π1 (θ0 )
68. 68. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Choice of density version c Dickey’s (1971) condition is not a condition: If π1 (θ0 |x) π0 (ψ)f (x|θ0 , ψ) dψ = π1 (θ0 ) m1 (x) is chosen as a version, then Savage–Dickey’s representation holds
69. 69. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Choice of density version c Dickey’s (1971) condition is not a condition: If π1 (θ0 |x) π0 (ψ)f (x|θ0 , ψ) dψ = π1 (θ0 ) m1 (x) is chosen as a version, then Savage–Dickey’s representation holds
70. 70. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Savage–Dickey paradox Verdinelli-Wasserman extension: π1 (θ0 |x) π1 (ψ|x,θ0 ,x) π0 (ψ) B01 = E π1 (θ0 ) π1 (ψ|θ0 ) similarly depends on choices of versions... ...but Monte Carlo implementation relies on speciﬁc versions of all densities without making mention of it [Chen, Shao & Ibrahim, 2000]
71. 71. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Savage–Dickey paradox Verdinelli-Wasserman extension: π1 (θ0 |x) π1 (ψ|x,θ0 ,x) π0 (ψ) B01 = E π1 (θ0 ) π1 (ψ|θ0 ) similarly depends on choices of versions... ...but Monte Carlo implementation relies on speciﬁc versions of all densities without making mention of it [Chen, Shao & Ibrahim, 2000]
72. 72. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Computational implementation Starting from the (new) prior π1 (θ, ψ) = π1 (θ)π0 (ψ) ˜ deﬁne the associated posterior π1 (θ, ψ|x) = π0 (ψ)π1 (θ)f (x|θ, ψ) m1 (x) ˜ ˜ and impose π1 (θ0 |x) ˜ π0 (ψ)f (x|θ0 , ψ) dψ = π0 (θ0 ) m1 (x) ˜ to hold. Then π1 (θ0 |x) m1 (x) ˜ ˜ B01 = π1 (θ0 ) m1 (x)
73. 73. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Computational implementation Starting from the (new) prior π1 (θ, ψ) = π1 (θ)π0 (ψ) ˜ deﬁne the associated posterior π1 (θ, ψ|x) = π0 (ψ)π1 (θ)f (x|θ, ψ) m1 (x) ˜ ˜ and impose π1 (θ0 |x) ˜ π0 (ψ)f (x|θ0 , ψ) dψ = π0 (θ0 ) m1 (x) ˜ to hold. Then π1 (θ0 |x) m1 (x) ˜ ˜ B01 = π1 (θ0 ) m1 (x)
74. 74. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio First ratio If (θ(1) , ψ (1) ), . . . , (θ(T ) , ψ (T ) ) ∼ π (θ, ψ|x), then ˜ 1 π1 (θ0 |x, ψ (t) ) ˜ T t converges to π1 (θ0 |x) (if the right version is used in θ0 ). ˜ π1 (θ0 )f (x|θ0 , ψ) π1 (θ0 |x, ψ) = ˜ π1 (θ)f (x|θ, ψ) dθ
75. 75. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Rao–Blackwellisation with latent variables When π1 (θ0 |x, ψ) unavailable, replace with ˜ T 1 π1 (θ0 |x, z (t) , ψ (t) ) ˜ T t=1 via data completion by latent variable z such that f (x|θ, ψ) = ˜ f (x, z|θ, ψ) dz ˜ and that π1 (θ, ψ, z|x) ∝ π0 (ψ)π1 (θ)f (x, z|θ, ψ) available in closed ˜ form, including the normalising constant, based on version π1 (θ0 |x, z, ψ) ˜ ˜ f (x, z|θ0 , ψ) = . π1 (θ0 ) ˜ f (x, z|θ, ψ)π1 (θ) dθ
76. 76. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Rao–Blackwellisation with latent variables When π1 (θ0 |x, ψ) unavailable, replace with ˜ T 1 π1 (θ0 |x, z (t) , ψ (t) ) ˜ T t=1 via data completion by latent variable z such that f (x|θ, ψ) = ˜ f (x, z|θ, ψ) dz ˜ and that π1 (θ, ψ, z|x) ∝ π0 (ψ)π1 (θ)f (x, z|θ, ψ) available in closed ˜ form, including the normalising constant, based on version π1 (θ0 |x, z, ψ) ˜ ˜ f (x, z|θ0 , ψ) = . π1 (θ0 ) ˜ f (x, z|θ, ψ)π1 (θ) dθ
77. 77. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Bridge revival (1) Since m1 (x)/m1 (x) is unknown, apparent failure! ˜ Use of the bridge identity π1 (θ, ψ)f (x|θ, ψ) π1 (ψ|θ) m1 (x) Eπ1 (θ,ψ|x) ˜ = Eπ1 (θ,ψ|x) ˜ = π0 (ψ)π1 (θ)f (x|θ, ψ) π0 (ψ) m1 (x) ˜ to (biasedly) estimate m1 (x)/m1 (x) by ˜ T π1 (ψ (t) |θ(t) ) T t=1 π0 (ψ (t) ) based on the same sample from π1 . ˜
78. 78. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Bridge revival (1) Since m1 (x)/m1 (x) is unknown, apparent failure! ˜ Use of the bridge identity π1 (θ, ψ)f (x|θ, ψ) π1 (ψ|θ) m1 (x) Eπ1 (θ,ψ|x) ˜ = Eπ1 (θ,ψ|x) ˜ = π0 (ψ)π1 (θ)f (x|θ, ψ) π0 (ψ) m1 (x) ˜ to (biasedly) estimate m1 (x)/m1 (x) by ˜ T π1 (ψ (t) |θ(t) ) T t=1 π0 (ψ (t) ) based on the same sample from π1 . ˜
79. 79. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Bridge revival (2) Alternative identity π0 (ψ)π1 (θ)f (x|θ, ψ) π0 (ψ) m1 (x) ˜ Eπ1 (θ,ψ|x) = Eπ1 (θ,ψ|x) = π1 (θ, ψ)f (x|θ, ψ) π1 (ψ|θ) m1 (x) ¯ ¯ suggests using a second sample (θ(1) , ψ (1) , z (1) ), . . . , ¯(T ) , ψ (T ) , z (T ) ) ∼ π1 (θ, ψ|x) and the ratio estimate (θ ¯ T 1 ¯ ¯ ¯ π0 (ψ (t) ) π1 (ψ (t) |θ(t) ) T t=1 Resulting unbiased estimate: (t) , ψ (t) ) T ¯ 1 t π1 (θ0 |x, z ˜ 1 π0 (ψ (t) ) B01 = T π1 (θ0 ) T t=1 π1 (ψ ¯ ¯ (t) |θ (t) )
80. 80. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Bridge revival (2) Alternative identity π0 (ψ)π1 (θ)f (x|θ, ψ) π0 (ψ) m1 (x) ˜ Eπ1 (θ,ψ|x) = Eπ1 (θ,ψ|x) = π1 (θ, ψ)f (x|θ, ψ) π1 (ψ|θ) m1 (x) ¯ ¯ suggests using a second sample (θ(1) , ψ (1) , z (1) ), . . . , ¯(T ) , ψ (T ) , z (T ) ) ∼ π1 (θ, ψ|x) and the ratio estimate (θ ¯ T 1 ¯ ¯ ¯ π0 (ψ (t) ) π1 (ψ (t) |θ(t) ) T t=1 Resulting unbiased estimate: (t) , ψ (t) ) T ¯ 1 t π1 (θ0 |x, z ˜ 1 π0 (ψ (t) ) B01 = T π1 (θ0 ) T t=1 π1 (ψ ¯ ¯ (t) |θ (t) )
81. 81. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Diﬀerence with Verdinelli–Wasserman representation The above leads to the representation π1 (θ0 |x) π1 (θ,ψ|x) π0 (ψ) ˜ B01 = E π1 (θ0 ) π1 (ψ|θ) which shows how our approach diﬀers from Verdinelli and Wasserman’s π1 (θ0 |x) π1 (ψ|x,θ0 ,x) π0 (ψ) B01 = E π1 (θ0 ) π1 (ψ|θ0 )
82. 82. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Diﬀerence with Verdinelli–Wasserman approximation In terms of implementation, (t) , ψ (t) ) T ¯ MR 1 t π1 (θ0 |x, z ˜ 1 π0 (ψ (t) ) B01 (x) = ¯ ¯ T π1 (θ0 ) T t=1 π1 (ψ (t) |θ(t) ) formaly resembles T T ˜ VW 1 π1 (θ0 |x, z (t) , ψ (t) ) 1 π0 (ψ (t) ) B01 (x) = . T π1 (θ0 ) T ˜ π1 (ψ (t) |θ0 ) t=1 t=1 But the simulated sequences diﬀer: ﬁrst average involves simulations from π1 (θ, ψ, z|x) and from π1 (θ, ψ, z|x), while second ˜ average relies on simulations from π1 (θ, ψ, z|x) and from π1 (ψ, z|x, θ0 ),
83. 83. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Diﬀerence with Verdinelli–Wasserman approximation In terms of implementation, (t) , ψ (t) ) T ¯ MR 1 t π1 (θ0 |x, z ˜ 1 π0 (ψ (t) ) B01 (x) = ¯ ¯ T π1 (θ0 ) T t=1 π1 (ψ (t) |θ(t) ) formaly resembles T T ˜ VW 1 π1 (θ0 |x, z (t) , ψ (t) ) 1 π0 (ψ (t) ) B01 (x) = . T π1 (θ0 ) T ˜ π1 (ψ (t) |θ0 ) t=1 t=1 But the simulated sequences diﬀer: ﬁrst average involves simulations from π1 (θ, ψ, z|x) and from π1 (θ, ψ, z|x), while second ˜ average relies on simulations from π1 (θ, ψ, z|x) and from π1 (ψ, z|x, θ0 ),
84. 84. Importance sampling methods for Bayesian discrimination between embedded models Importance sampling model comparison solutions compared The Savage–Dickey ratio Diabetes in Pima Indian women (cont’d) Comparison of the variation of the Bayes factor approximations based on 100 replicas for 20, 000 simulations for a simulation from the above importance, Chib’s, Savage–Dickey’s and bridge samplers q 3.4 3.2 3.0 2.8 q IS Chib Savage−Dickey Bridge