/// <summary>
/// Get the average logarithm of that distribution under this distribution, i.e. int this(x) log( that(x) ) dx
/// </summary>
/// <param name="that"></param>
/// <returns></returns>
public double GetAverageLog(TruncatedGaussian that)
{
if (IsPointMass)
{
if (that.IsPointMass && this.Point == that.Point)
{
return(0.0);
}
else
{
return(that.GetLogProb(this.Point));
}
}
else if (this.LowerBound < that.LowerBound || this.UpperBound > that.UpperBound)
{
return(double.NegativeInfinity);
}
else if (that.IsPointMass)
{
return(Double.NegativeInfinity);
}
else if (!IsProper())
{
throw new ImproperDistributionException(this);
}
else
{
double m, v;
GetMeanAndVariance(out m, out v);
return(-0.5 * that.Gaussian.Precision * (m * m + v) + that.Gaussian.MeanTimesPrecision * m - that.GetLogNormalizer());
}
}
/// <summary>
/// Get the logarithm of the average value of that distribution under this distribution, i.e. log(int this(x) that(x) dx)
/// </summary>
/// <param name="that"></param>
/// <returns></returns>
public double GetLogAverageOf(TruncatedGaussian that)
{
if (IsPointMass)
{
return(that.GetLogProb(Point));
}
else if (that.IsPointMass)
{
return(GetLogProb(that.Point));
}
else
{
// neither this nor that is a point mass.
TruncatedGaussian product = this * that;
return(product.GetLogNormalizer() - this.GetLogNormalizer() - that.GetLogNormalizer());
}
}
/// <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(TruncatedGaussian that, double power)
{
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
{
var product = this * (that ^ power);
return(product.GetLogNormalizer() - this.GetLogNormalizer() - power * that.GetLogNormalizer());
}
}