/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="GammaRatioOp_Laplace"]/message_doc[@name="BAverageConditional(Gamma, Gamma, Gamma, Gamma)"]/*'/>
public static Gamma BAverageConditional([SkipIfUniform] Gamma ratio, [Proper] Gamma A, [Proper] Gamma B, Gamma q)
{
if (ratio.IsPointMass)
{
// AAverageConditional computes (1st arg)/(2nd arg)
return(GammaRatioOp.BAverageConditional(ratio.Point, A));
}
if (B.IsPointMass)
{
throw new NotImplementedException();
}
if (q.IsUniform())
{
q = Q(ratio, A, B);
}
double x = q.GetMean();
double[] g = new double[] { x, 1, 0, 0 };
double bMean, bVariance;
GaussianOp_Laplace.LaplaceMoments(q, g, dlogfs(x, ratio, A), out bMean, out bVariance);
Gamma bMarginal = Gamma.FromMeanAndVariance(bMean, bVariance);
Gamma result = new Gamma();
result.SetToRatio(bMarginal, B, GammaProductOp_Laplace.ForceProper);
return(result);
}
public static Gamma Q(Gamma ratio, [Proper] Gamma A, [Proper] Gamma B)
{
if (B.IsPointMass)
{
return(B);
}
if (ratio.IsPointMass)
{
return(GammaRatioOp.BAverageConditional(ratio.Point, A) * B);
}
double shape1 = A.Shape + B.Shape;
double shape2 = GammaFromShapeAndRateOp_Slow.AddShapesMinus1(A.Shape, ratio.Shape);
// find the maximum of the factor marginalized over Ratio and A, times B
// logf = s*log(b) - (s+ya-1)*log(b*r + yb)
// let b' = b*r and maximize over b'
double x = GammaFromShapeAndRateOp_Slow.FindMaximum(shape1, shape2, ratio.Rate, B.Rate / A.Rate);
if (x == 0)
{
return(B);
}
x /= A.Rate;
double[] dlogfss = dlogfs(x, ratio, A);
double dlogf = dlogfss[0];
double ddlogf = dlogfss[1];
return(GammaFromShapeAndRateOp_Laplace.GammaFromDerivatives(B, x, dlogf, ddlogf));
}
