Chapter 3 Posteriors

3.1 Posterior for τ

  • p(τ|μ1,,μm,y,α,β,k1)

p(τ|μ1,,μm,y,α,β,k1)τα1exp{βτ}×(τ/k1)m/2exp{τ2k1mj=1(μjμ)2}×τn/2exp{τ2mj=1nji=1(yijμj)2}×(τ/k2)1/2exp{τ2k2μ2}τ(n+m+1)/2+α1exp{τ(mj=1nji=1(yijμj)22+β+mj=1(μjμ)22k1+μ22k2)}

So τ|μ1,,μm,y,α,β,k1,k2Gamma((n+m+1)/2+α,(mj=1nji=1(yijμj)22+β+mj=1(μjμ)22k1+μ22k2))

3.2 Posterior for μj

Q=(τ/k1)mj=1(μjμ)2+τmj=1nji=1(yijμj)2Q=τ[mj=1njμ2j+μ2jk1(mj=12μμjk1+2ˉyjnjμj)]Qτ[mj=1(nj+1k1)μ2j2μj(μk1+ˉyjnj)]Q[mj=1(nj+1k1)(μjμ/k1+ˉyjnjnj+1/k1)2] So for each μj

μj|μ,y,τ,k1N(μ/k1+ˉyjnjnj+1/k1,((nj+1k1)τ)1)

3.3 Posterior for μ

Similarly, for μ we have:

Q=τk1mj=1(μjμ)2+τk2μ2Q=τk1mj=1(μ2j2μμj+μ2)+τk2μ2Q(τk2+τk1m)μ22τk1mj=1μμjQ(τk2+τk1m)μ22τk1μˉμmQ(τ(mk1+1k2))(μ(τ/k1)ˉμmτ(mk1+1k2))2

So for μ we have that the conditional distribution:

μ|μ1,,μm,μμ,k1,k2,τN((τ/k1)ˉμmτ(mk1+1k2),(τ(mk1+1k2))1)