/// <summary>
/// Get the integral of this distribution times another distribution raised to a power.
/// </summary>
/// <param name="that"></param>
/// <param name="power"></param>
/// <returns></returns>
public double GetLogAverageOfPower(SparseGP that, double power)
{
throw new NotImplementedException();
if (IsPointMass)
{
return(power * that.GetLogProb(Point));
}
else if (that.IsPointMass)
{
if (power < 0)
{
throw new DivideByZeroException("The exponent is negative and the distribution is a point mass");
}
return(this.GetLogProb(that.Point));
}
else
{
}
}
/// <summary>
/// Gets the log of the integral of the product of this SparseGP and that SparseGP
/// </summary>
/// <param name="that"></param>
/// <returns></returns>
public double GetLogAverageOf(SparseGP that)
{
if (this.FixedParameters != that.FixedParameters)
{
throw new ArgumentException("SparseGPs do not have the same FixedParameters. this.FixedParameters = " + this.FixedParameters + ", that.FixedParameters = " + that.FixedParameters);
}
if (this.IncludePrior && that.IncludePrior)
{
throw new ArgumentException("Both SparseGPs include the prior");
}
if (that.IsPointMass)
{
return(GetLogProb(that.Point));
}
if (this.IsPointMass)
{
return(that.GetLogProb(this.Point));
}
if (this.IncludePrior && !that.IncludePrior)
{
// gBB is the distribution of the function on the basis
VectorGaussian gBB;
if (true)
{
gBB = new VectorGaussian(InducingDist.Dimension);
gBB.Precision.SetToSum(FixedParameters.InvKernelOf_B_B, InducingDist.Precision);
gBB.MeanTimesPrecision.SetTo(InducingDist.MeanTimesPrecision); // since prior has zero mean
}
else
{
// equivalent but slower
gBB = VectorGaussian.FromMeanAndVariance(Mean_B, Var_B_B);
}
return(gBB.GetLogAverageOf(that.InducingDist));
}
if (!this.IncludePrior && that.IncludePrior)
{
return(that.GetLogAverageOf(this));
}
throw new NotImplementedException();
}
