Comments (2)
Our general stance is against unnecessary normalization, in the statistics field in general. The logsumexp
of the log_weights
is a meaningful quantity, and can be used for a variety of tasks:
- as a loss to backprop through
- as an expert log weight in a mixture of experts model, e.g. an
SMCFilter
and a non-normalized Gaussian (as in our pyro.ops.Gaussian library or Funsor) - as a measure of goodness of fit
from pyro.
Sorry, I misread. I do think it's fine to implement a .normalized_weights()
method or property, as long as we preserve the original unnormalized weights.
from pyro.
Related Issues (20)
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