/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="GammaPowerProductOp_Laplace"]/message_doc[@name="BAverageConditional(GammaPower, GammaPower, GammaPower, Gamma, GammaPower)"]/*'/>
public static GammaPower BAverageConditional([SkipIfUniform] GammaPower product, [Proper] GammaPower A, [Proper] GammaPower B, Gamma q, GammaPower result)
{
if (B.Shape < A.Shape)
{
return(AAverageConditional(product, B, A, q, result));
}
if (A.IsPointMass)
{
return(GammaProductOp.BAverageConditional(product, A.Point, result));
}
if (B.IsPointMass)
{
return(GammaPower.Uniform(result.Power)); // TODO
}
if (product.IsUniform())
{
return(product);
}
if (q.IsUniform())
{
q = Q(product, A, B);
}
double bPoint = q.GetMean();
// derivatives of b
double[] bDerivatives = new double[] { bPoint, 1, 0, 0 };
double bMean, bVariance;
GaussianOp_Laplace.LaplaceMoments(q, bDerivatives, dlogfs(bPoint, product, A), out bMean, out bVariance);
GammaPower bMarginal = GammaPower.FromGamma(Gamma.FromMeanAndVariance(bMean, bVariance), result.Power);
result.SetToRatio(bMarginal, B, GammaProductOp_Laplace.ForceProper);
return(result);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="GammaPowerProductOp_Laplace"]/message_doc[@name="BAverageConditional(GammaPower, GammaPower, GammaPower, Gamma, GammaPower)"]/*'/>
public static GammaPower BAverageConditional([SkipIfUniform] GammaPower product, [Proper] GammaPower A, [Proper] GammaPower B, Gamma q, GammaPower result)
{
if (B.Shape < A.Shape)
{
return(AAverageConditional(product, B, A, q, result));
}
if (A.IsPointMass)
{
return(GammaProductOp.BAverageConditional(product, A.Point, result));
}
if (B.IsPointMass)
{
return(GammaPower.Uniform(result.Power)); // TODO
}
if (product.IsUniform())
{
return(product);
}
if (q.IsUniform())
{
q = Q(product, A, B);
}
double qPoint = q.GetMean();
GammaPower bMarginal;
// threshold ensures 6/qPoint^4 does not overflow
double threshold = Math.Sqrt(Math.Sqrt(6 / double.MaxValue));
if (result.Power < 0 && qPoint > threshold)
{
double iqMean, iqVariance;
GetIQMoments(product, A, q, qPoint, out iqMean, out iqVariance);
GammaPower iqMarginal = GammaPower.FromMeanAndVariance(iqMean, iqVariance, -1);
bMarginal = GammaPower.FromShapeAndRate(iqMarginal.Shape, iqMarginal.Rate, result.Power);
}
else
{
// B.Shape >= A.Shape therefore Q is the approximate distribution of B^(1/B.Power).
// We compute the approximate moments of q = b^(1/b.Power) to get a Gamma distribution and then raise to B.Power.
double qMean, qVariance;
GetQMoments(product, A, q, qPoint, out qMean, out qVariance);
bMarginal = GammaPower.FromGamma(Gamma.FromMeanAndVariance(qMean, qVariance), result.Power);
}
result.SetToRatio(bMarginal, B, GammaProductOp_Laplace.ForceProper);
return(result);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="ProductGaussianGammaVmpOp"]/message_doc[@name="BAverageLogarithm(double, double)"]/*'/>
public static Gamma BAverageLogarithm(double Product, double A)
{
return(GammaProductOp.BAverageConditional(Product, A));
}
