How to sample from a truncated normal distribution in PyTorch? I could not find any within https://pytorch.org/docs/stable/distributions.html. Is it not implemented? Any workarounds?

This post might be the easiest approach.

In the topic were several methods discussed, so another one might fit your use case better.

I put together a truncated normal distribution class, implementing torch.distributions.Distribution interface: https://github.com/toshas/torch_truncnorm

I wanted to follow up on this as I am looking `rsample`

from a truncated Gaussian in PyTorch and compute `log_prob`

and wanted to see if there were any updated developments. @anton’s module that he shared is very helpful, but unfortunately, I am looking for a solution that is CUDA-capable. Thank you for your help.

It is CUDA-capable now

Yes, made the post before our email exchange. Thanks again for your help!

Is it also differentiable in the parameters?

Why not add this to PyTorch?

I am looking for a way to sample the truncated normal that is *differentiable* in the parameters of the distribution. That post doesn’t cover automatic differentiation, as far as I can see.

Has anyone got this working in a similar fashion to Tensorflow’s `tensorflow.distributions.TruncatedMultivariateNormal`

?

See here tfp.distributions.MultivariateNormalTriL | TensorFlow Probability.

For my application I specifically need to specify the covariance and location of the mean, so something more detailed than a standard unit normal.

I understand it may be possible to sample from an un-truncated multivariate normal, multiply it by the covariance, then truncate, but I would rather a simple and proper way of doing it. Thanks all!