Add Wishart distribution. #1779
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fehiepsi
reviewed
Apr 12, 2024
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Thanks @tillahoffmann! It is great to have this distribution. I left initial comments below.
tillahoffmann
commented
Apr 13, 2024
fehiepsi
reviewed
May 6, 2024
fehiepsi
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May 13, 2024
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Thanks, @tillahoffmann! Just have a comment at the computation of slogdet.
| batch_shape, event_shape = loc[:-1], loc[-1:] | ||
| for matrix in [covariance_matrix, precision_matrix, scale_tril]: | ||
| if matrix is not None: | ||
| batch_shape = lax.broadcast_shapes(batch_shape, matrix[:-2]) | ||
| event_shape = lax.broadcast_shapes(event_shape, matrix[-1:]) | ||
| return batch_shape, event_shape | ||
| return batch_shape, event_shape |
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Yes, assert_one_of ensures that exactly one of the matrices is available, and returning early avoids looping over precision_matrix and scale_tril if covariance_matrix is given, for example. Either with/without the indent should work though.
fehiepsi
approved these changes
May 13, 2024
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This PR adds the Wishart distribution for covariance matrices. It should probably be rebased and reviewed after #1778 is merged.