/// <summary>
/// VMP message to 'precision'
/// </summary>
/// <param name="sample">Constant value for 'sample'.</param>
/// <param name="mean">Incoming message from 'mean'. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <returns>The outgoing VMP message to the 'precision' argument</returns>
/// <remarks><para>
/// The outgoing message is the exponential of the average log-factor value, where the average is over all arguments except 'precision'.
/// The formula is <c>exp(sum_(mean) p(mean) log(factor(sample,mean,precision)))</c>.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="mean"/> is not a proper distribution</exception>
public static SparseGammaList PrecisionAverageLogarithm(IList<double> sample, [Proper]SparseGaussianList mean, SparseGammaList result)
{
result.SetToFunction(sample, mean, (s, m) => GaussianOp.PrecisionAverageLogarithm(s, m));
return result;
}
/// <summary>
/// EP message to 'precision'
/// </summary>
/// <param name="sample">Constant value for 'sample'.</param>
/// <param name="mean">Constant value for 'mean'.</param>
/// <returns>The outgoing EP message to the 'precision' argument</returns>
/// <remarks><para>
/// The outgoing message is the factor viewed as a function of 'precision' conditioned on the given values.
/// </para></remarks>
public static SparseGammaList PrecisionAverageConditional(IList<double> sample, IList<double> mean, SparseGammaList result)
{
result.SetToFunction(sample, mean, (s, m) => GaussianOp.PrecisionAverageConditional(s, m));
return result;
}
/// <summary>
/// EP message to 'precision'
/// </summary>
/// <param name="sample">Incoming message from 'sample'. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <param name="mean">Incoming message from 'mean'. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <param name="precision">Incoming message from 'precision'. Must be a proper distribution. If uniform, the result will be uniform.</param>
/// <returns>The outgoing EP message to the 'precision' argument</returns>
/// <remarks><para>
/// The outgoing message is a distribution matching the moments of 'precision' as the random arguments are varied.
/// The formula is <c>proj[p(precision) sum_(sample,mean) p(sample,mean) factor(sample,mean,precision)]/p(precision)</c>.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="sample"/> is not a proper distribution</exception>
/// <exception cref="ImproperMessageException"><paramref name="mean"/> is not a proper distribution</exception>
/// <exception cref="ImproperMessageException"><paramref name="precision"/> is not a proper distribution</exception>
public static SparseGammaList PrecisionAverageConditional([SkipIfUniform] SparseGaussianList sample, [SkipIfUniform] SparseGaussianList mean, [SkipIfUniform] SparseGammaList precision, SparseGammaList result)
{
result.SetToFunction(sample, mean, precision, (s, m, p) => GaussianOp.PrecisionAverageConditional_slow(s, m, p));
return result;
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="SparseGaussianListOp"]/message_doc[@name="PrecisionAverageConditional(ISparseList{double}, SparseGaussianList, SparseGammaList, SparseGammaList)"]/*'/>
public static SparseGammaList PrecisionAverageConditional(
ISparseList <double> sample, [SkipIfUniform] SparseGaussianList mean, [SkipIfUniform] SparseGammaList precision, SparseGammaList result)
{
result.SetToFunction(sample, mean, precision, (s, m, p) => GaussianOp.PrecisionAverageConditional_slow(Gaussian.PointMass(s), m, p));
return(result);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="SparseGaussianListOp"]/message_doc[@name="PrecisionAverageConditional(ISparseList{double}, ISparseList{double}, SparseGammaList)"]/*'/>
public static SparseGammaList PrecisionAverageConditional(ISparseList <double> sample, ISparseList <double> mean, SparseGammaList result)
{
result.SetToFunction(sample, mean, (s, m) => GaussianOp.PrecisionAverageConditional(s, m));
return(result);
}
/// <include file='FactorDocs.xml' path='factor_docs/message_op_class[@name="SparseGaussianListOp"]/message_doc[@name="PrecisionAverageLogarithm(ISparseList{double}, SparseGaussianList, SparseGammaList)"]/*'/>
public static SparseGammaList PrecisionAverageLogarithm(ISparseList <double> sample, [Proper] SparseGaussianList mean, SparseGammaList result)
{
result.SetToFunction(sample, mean, (s, m) => GaussianOp.PrecisionAverageLogarithm(s, m));
return(result);
}