/// <summary>
/// Gradient matching VMP message from factor to logOdds variable
/// </summary>
/// <param name="sample">Incoming message from 'sample'.</param>
/// <param name="logOdds">Incoming message. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <param name="result">Previous message sent, used for damping</param>
/// <returns>The outgoing VMP message.</returns>
/// <remarks><para>
/// The outgoing message is the Gaussian approximation to the factor which results in the
/// same derivatives of the KL(q||p) divergence with respect to the parameters of the posterior
/// as if the true factor had been used.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="logOdds"/> is not a proper distribution</exception>
public static Gaussian LogOddsAverageLogarithm(Bernoulli sample, [Proper, SkipIfUniform] Gaussian logOdds, Gaussian result)
{
if (sample.IsUniform())
{
return(Gaussian.Uniform());
}
throw new NotImplementedException("BernoulliFromLogOdds with non-observed output is not yet implemented");
}
public static TruncatedGaussian XAverageConditional([SkipIfUniform] Bernoulli isPositive)
{
if (isPositive.IsUniform())
{
return(TruncatedGaussian.Uniform());
}
if (isPositive.IsPointMass)
{
return(XAverageConditional(isPositive.Point));
}
throw new NotSupportedException("Cannot return a TruncatedGaussian when isPositive is random");
}
public static Gaussian XAverageLogarithm([SkipIfUniform] Bernoulli isPositive, [SkipIfUniform, Stochastic] Gaussian x, Gaussian to_X)
{
if (isPositive.IsPointMass)
{
return(XAverageLogarithm(isPositive.Point, x, to_X));
}
if (isPositive.IsUniform())
{
return(Gaussian.Uniform());
}
throw new NotSupportedException(NotSupportedMessage2);
var prior = x / to_X;
return(XAverageConditional(isPositive, prior));
}
/// <summary>
/// Gradient matching VMP message from factor to logOdds variable
/// </summary>
/// <param name="sample">Incoming message from 'sample'.</param>
/// <param name="logOdds">Incoming message. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <param name="result">Previous message sent, used for damping</param>
/// <returns>The outgoing VMP message.</returns>
/// <remarks><para>
/// The outgoing message is the Gaussian approximation to the factor which results in the
/// same derivatives of the KL(q||p) divergence with respect to the parameters of the posterior
/// as if the true factor had been used.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="logOdds"/> is not a proper distribution</exception>
public static Gaussian LogOddsAverageLogarithm(Bernoulli sample, [Proper, SkipIfUniform] Gaussian logOdds, Gaussian result)
{
if (sample.IsUniform()) return Gaussian.Uniform();
throw new NotImplementedException("BernoulliFromLogOdds with non-observed output is not yet implemented");
}