/// <summary>
/// EP message to 'fromArray'
/// </summary>
/// <param name="array">Incoming message from 'data'. Must be a proper distribution. If all elements are uniform, the result will be uniform.</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 'fromArray' as the random arguments are varied.
/// The formula is <c>proj[p(fromArray) sum_(data) p(data) factor(fromArray,data)]/p(fromArray)</c>.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="array"/> is not a proper distribution</exception>
// TM: SkipIfAllUniform would be more accurate but leads to half-uniform distributions
public static VectorGaussian VectorAverageConditional([SkipIfAnyUniform] IList <Gaussian> array, VectorGaussian result)
{
return(ArrayFromVectorOp.VectorAverageConditional(array, result));
}
/// <summary>
/// EP message to 'data'
/// </summary>
/// <param name="vector">Incoming message from 'fromArray'. Must be a proper distribution. If any element is uniform, the result will be uniform.</param>
/// <param name="array">Incoming message from 'data'.</param>
/// <param name="to_vector">Outgoing message to 'vector'.</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 'data' as the random arguments are varied.
/// The formula is <c>proj[p(data) sum_(fromArray) p(fromArray) factor(fromArray,data)]/p(data)</c>.
/// </para></remarks>
/// <exception cref="ImproperMessageException"><paramref name="vector"/> is not a proper distribution</exception>
public static GaussianList ArrayAverageConditional <GaussianList>([SkipIfUniform] VectorGaussian vector, IList <Gaussian> array, [Fresh] VectorGaussian to_vector, GaussianList result)
where GaussianList : IList <Gaussian>
{
return(ArrayFromVectorOp.ArrayAverageConditional <GaussianList>(array, vector, to_vector, result));
}
/// <summary>EP message to <c>fromArray</c>.</summary>
/// <param name="array">Incoming message from <c>data</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>fromArray</c> as the random arguments are varied. The formula is <c>proj[p(fromArray) sum_(data) p(data) factor(fromArray,data)]/p(fromArray)</c>.</para>
/// </remarks>
public static VectorGaussian VectorAverageConditional(
[SkipIfAnyUniform] IList <Gaussian> array, VectorGaussian result) // TM: SkipIfAllUniform would be more accurate but leads to half-uniform distributions
{
return(ArrayFromVectorOp.VectorAverageConditional(array, result));
}
