Comments (1)
To solve this issue I propose the following:
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On point 1, instead of modelling and sampling the whole covariance matrix, do it with just the lower/upper half over the diagonal, and when creating the model from sampled parameters, completing the other half using simetry over diagonal, that is:
matrix[i][j] = matrix[j][i]
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For the second point, I will transform the standard deviation using the positive transformer mentioned here before modelling and reverse transform it when recreating the model.
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