Theta Mixture Prior

src/expectation_propagation.jl

@@ -153,7 +153,7 @@ function expectation_propagation(H::AbstractVector{Term{T}}, P0::AbstractVector{
         for i in 1:length(H)
             updateβ(H[i], av[1:Nx])
         end
-        ret = callback(av,Δav,epsconv,maxiter,H,P0)
+        ret = callback(iter,state,Δav,Δva,epsconv,maxiter,H,P0)
         if ret === true || (Δav < epsconv && norm(F*av[1:Nx]+d-av[Nx+1:end]) < 1e-4)
             return EPOut(state, :converged)
         end

src/priors.jl

@@ -298,3 +298,34 @@ function moments(::ThetaPrior,μ,σ)
     return av,var
 end
 
+"""
+A mixture of theta priors: p_0(x)=η*Θ(x)+(1-η)*Θ(-x)
+"""
+mutable struct ThetaMixturePrior{T<:Real} <: Prior
+    η::T
+    δη::T
+end
+
+function theta_mixt_factor(x,η)
+    f=exp(-0.5*x^2.0)/(η*erfc(-sqrt(0.5)*x)+(1.0-η)*erfc(sqrt(0.5)*x))
+    return f
+end
+
+function moments(p0::ThetaMixturePrior,μ,σ)
+    η=p0.η
+    α=μ/σ
+    f=theta_mixt_factor(α,η)
+    χ=sqrt(2.0/π)*(2.0*η-1.0)*f
+    av=μ+σ*χ
+    va=σ^2.0*(1-χ^2.0)-μ*σ*χ
+    return av,va
+end
+
+function gradient(p0::ThetaMixturePrior,μ,σ)
+    η=p0.η
+    x=μ/σ/sqrt(2)
+    num=2*erf(x)
+    den=η*erfc(-x)+(1-η)*erfc(x)
+    p0.η+=p0.δη*num/den
+    p0.η=clamp(p0.η,0,1)
+end