/// <summary>VMP message to <c>b</c>.</summary>
/// <param name="areEqual">Constant value for <c>areEqual</c>.</param>
/// <param name="A">Incoming message from <c>a</c>.</param>
/// <param name="result">Modified to contain the outgoing message.</param>
/// <returns>
/// <paramref name="result" />
/// </returns>
/// <remarks>
/// <para>The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except <c>b</c>. The formula is <c>exp(sum_(a) p(a) log(factor(areEqual,a,b)))</c>.</para>
/// </remarks>
public static DiscreteEnum <TEnum> BAverageLogarithm(bool areEqual, TEnum A, DiscreteEnum <TEnum> result)
{
return(DiscreteEnum <TEnum> .FromDiscrete(DiscreteAreEqualOp.BAverageLogarithm(areEqual, ToInt(A), result.GetInternalDiscrete())));
}
/// <summary>EP message to <c>a</c>.</summary>
/// <param name="areEqual">Constant value for <c>areEqual</c>.</param>
/// <param name="B">Incoming message from <c>b</c>.</param>
/// <param name="result">Modified to contain the outgoing message.</param>
/// <returns>
/// <paramref name="result" />
/// </returns>
/// <remarks>
/// <para>The outgoing message is a distribution matching the moments of <c>a</c> as the random arguments are varied. The formula is <c>proj[p(a) sum_(b) p(b) factor(areEqual,a,b)]/p(a)</c>.</para>
/// </remarks>
public static DiscreteEnum <TEnum> AAverageConditional(bool areEqual, TEnum B, DiscreteEnum <TEnum> result)
{
return(DiscreteEnum <TEnum> .FromDiscrete(DiscreteAreEqualOp.AAverageConditional(areEqual, ToInt(B), result.GetInternalDiscrete())));
}
/// <summary>VMP message to <c>b</c>.</summary>
/// <param name="areEqual">Incoming message from <c>areEqual</c>. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <param name="A">Incoming message from <c>a</c>.</param>
/// <param name="result">Modified to contain the outgoing message.</param>
/// <returns>
/// <paramref name="result" />
/// </returns>
/// <remarks>
/// <para>The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except <c>b</c>. Because the factor is deterministic, <c>areEqual</c> is integrated out before taking the logarithm. The formula is <c>exp(sum_(a) p(a) log(sum_areEqual p(areEqual) factor(areEqual,a,b)))</c>.</para>
/// </remarks>
/// <exception cref="ImproperMessageException">
/// <paramref name="areEqual" /> is not a proper distribution.</exception>
public static DiscreteEnum <TEnum> BAverageLogarithm([SkipIfUniform] Bernoulli areEqual, TEnum A, DiscreteEnum <TEnum> result)
{
return(DiscreteEnum <TEnum> .FromDiscrete(DiscreteAreEqualOp.BAverageLogarithm(areEqual, ToInt(A), result.GetInternalDiscrete())));
}
/// <summary>EP message to <c>a</c>.</summary>
/// <param name="areEqual">Incoming message from <c>areEqual</c>. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <param name="B">Incoming message from <c>b</c>.</param>
/// <param name="result">Modified to contain the outgoing message.</param>
/// <returns>
/// <paramref name="result" />
/// </returns>
/// <remarks>
/// <para>The outgoing message is a distribution matching the moments of <c>a</c> as the random arguments are varied. The formula is <c>proj[p(a) sum_(areEqual,b) p(areEqual,b) factor(areEqual,a,b)]/p(a)</c>.</para>
/// </remarks>
/// <exception cref="ImproperMessageException">
/// <paramref name="areEqual" /> is not a proper distribution.</exception>
public static DiscreteEnum <TEnum> AAverageConditional([SkipIfUniform] Bernoulli areEqual, TEnum B, DiscreteEnum <TEnum> result)
{
return(DiscreteEnum <TEnum> .FromDiscrete(DiscreteAreEqualOp.AAverageConditional(areEqual, ToInt(B), result.GetInternalDiscrete())));
}