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Might be worth adding a laplace.dense_hessian method since the Hessian is easy to compute with torch.func.hessian.
The main problem with this is that the Hessian is not guaranteed to be positive semidefinite, however there may be cases where it is i.e. when we are sure we have optimized exactly to the MAP or simple models. We should add an epsilon parameter though that adds a nugget epsilon * torch.eye(dim) to the total Hessian that will ensure positive definiteness for epilson large enough.
I think we should should not add a laplace.diag_hessian method since it is not easy to compute in contrast to the Fisher methods.
The text was updated successfully, but these errors were encountered:
Might be worth adding a
laplace.dense_hessian
method since the Hessian is easy to compute withtorch.func.hessian
.The main problem with this is that the Hessian is not guaranteed to be positive semidefinite, however there may be cases where it is i.e. when we are sure we have optimized exactly to the MAP or simple models. We should add an
epsilon
parameter though that adds a nuggetepsilon * torch.eye(dim)
to the total Hessian that will ensure positive definiteness forepilson
large enough.I think we should should not add a
laplace.diag_hessian
method since it is not easy to compute in contrast to the Fisher methods.The text was updated successfully, but these errors were encountered: