/// <summary>EP message to <c>double</c>.</summary>
/// <param name="Double">Incoming message from <c>double</c>.</param>
/// <param name="Integer">Incoming message from <c>integer</c>.</param>
/// <returns>The outgoing EP message to the <c>double</c> argument.</returns>
/// <remarks>
/// <para>The outgoing message is a distribution matching the moments of <c>double</c> as the random arguments are varied. The formula is <c>proj[p(double) sum_(integer) p(integer) factor(double,integer)]/p(double)</c>.</para>
/// </remarks>
public static Gaussian DoubleAverageConditional(Gaussian Double, Discrete Integer)
{
if (Integer.IsPointMass)
{
return(Gaussian.PointMass(Factor.Double(Integer.Point)));
}
// Z = sum_i int_x q(x) delta(x - i) q(i) dx
// = sum_i q(x=i) q(i)
double max = double.NegativeInfinity;
for (int i = 0; i < Integer.Dimension; i++)
{
double logp = Double.GetLogProb(i);
if (logp > max)
{
max = logp;
}
}
if (double.IsNegativeInfinity(max))
{
throw new AllZeroException();
}
GaussianEstimator est = new GaussianEstimator();
for (int i = 0; i < Integer.Dimension; i++)
{
double logp = Double.GetLogProb(i);
est.Add(i, Integer[i] * Math.Exp(logp - max));
}
Gaussian result = est.GetDistribution(new Gaussian());
result.SetToRatio(result, Double, ForceProper);
return(result);
}
/// <summary>Evidence message for EP.</summary>
/// <param name="Double">Constant value for <c>double</c>.</param>
/// <param name="Integer">Constant value for <c>integer</c>.</param>
/// <returns>Logarithm of the factor's contribution the EP model evidence.</returns>
/// <remarks>
/// <para>The formula for the result is <c>log(factor(double,integer))</c>. Adding up these values across all factors and variables gives the log-evidence estimate for EP.</para>
/// </remarks>
public static double LogEvidenceRatio(double Double, int Integer)
{
return((Double == Factor.Double(Integer)) ? 0.0 : double.NegativeInfinity);
}