/// <summary>
/// Evidence message for EP
/// </summary>
/// <param name="isPositive">Incoming message from 'isPositive'.</param>
/// <param name="x">Incoming message from 'x'.</param>
/// <returns>Logarithm of the factor's average value across the given argument distributions</returns>
/// <remarks><para>
/// The formula for the result is <c>log(sum_(isPositive,x) p(isPositive,x) factor(isPositive,x))</c>.
/// </para></remarks>
public static double LogAverageFactor(Bernoulli isPositive, Gaussian x)
{
Bernoulli to_isPositive = IsPositiveAverageConditional(x);
return(isPositive.GetLogAverageOf(to_isPositive));
#if false
// Z = p(b=T) p(x > 0) + p(b=F) p(x <= 0)
// = p(b=F) + (p(b=T) - p(b=F)) p(x > 0)
if (x.IsPointMass)
{
return(Factor.IsPositive(x.Point) ? isPositive.GetLogProbTrue() : isPositive.GetLogProbFalse());
}
else if (x.IsUniform())
{
return(Bernoulli.LogProbEqual(isPositive.LogOdds, 0.0));
}
else
{
// m/sqrt(v) = (m/v)/sqrt(1/v)
double z = x.MeanTimesPrecision / Math.Sqrt(x.Precision);
if (isPositive.IsPointMass)
{
return(isPositive.Point ? MMath.NormalCdfLn(z) : MMath.NormalCdfLn(-z));
}
else
{
return(MMath.LogSumExp(isPositive.GetLogProbTrue() + MMath.NormalCdfLn(z), isPositive.GetLogProbFalse() + MMath.NormalCdfLn(-z)));
}
}
#endif
}