jax
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Currently, in AutoLaplaceApproximation, we use jax.hessian to compute Hessian matrix. But the hessian matrix is not always positive definite. So it would be nice to use Gaussian-Newton hessian matrix when scale_tril = Nan.
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Description
At the PyHEP April 2021 topical meeting pyhf tutorial, @BlaiseDelaney had the following question on pertaining to the Normalisation Uncertainty (normsys) docs:
I'm a bit confused about what I need to supply to the modifier. Is it the actual nume
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See wikipedia table for example patterns: https://en.wikipedia.org/wiki/List_of_integrals_of_Gaussian_functions
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Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?