/// <summary>
/// Update the buffer 'CovarianceOfB'
/// </summary>
/// <param name="B">Incoming message from 'B'. Must be a proper distribution. If any element is uniform, the result will be uniform.</param>
/// <returns>New value of buffer 'CovarianceOfB'</returns>
/// <exception cref="ImproperMessageException"><paramref name="B"/> is not a proper distribution</exception>
public static PositiveDefiniteMatrix Eaat(GaussianArray A, PositiveDefiniteMatrix result)
{
int inner = A.Count;
var VarianceOfA = Vector.Zero(inner);
var MeanOfA = Vector.Zero(inner);
for (int k = 0; k < inner; k++) {
MeanOfA[k] = A[k].GetMean();
VarianceOfA[k] = A[k].GetVariance();
}
result.SetToDiagonal(VarianceOfA);
result.SetToSumWithOuter(result, 1, MeanOfA, MeanOfA);
return result;
}
/// <summary>
/// Update the buffer 'CovarianceOfB'
/// </summary>
/// <param name="B">Incoming message from 'B'. Must be a proper distribution. If any element is uniform, the result will be uniform.</param>
/// <returns>New value of buffer 'CovarianceOfB'</returns>
/// <exception cref="ImproperMessageException"><paramref name="B"/> is not a proper distribution</exception>
public static PositiveDefiniteMatrix Eaat(GaussianArray A, PositiveDefiniteMatrix result)
{
int inner = A.Count;
var VarianceOfA = Vector.Zero(inner);
var MeanOfA = Vector.Zero(inner);
for (int k = 0; k < inner; k++)
{
MeanOfA[k] = A[k].GetMean();
VarianceOfA[k] = A[k].GetVariance();
}
result.SetToDiagonal(VarianceOfA);
result.SetToSumWithOuter(result, 1, MeanOfA, MeanOfA);
return(result);
}
