I have two implementations of a numerical Bayesian inference algorithm written in Numpy and Torch and would like to interface them with NumPyro and Pyro, respectively. With NumPyro, I have leveraged the `log_density`

method of `numpyro.infer.util`

to calculate the log probability of a model and Jax’s autodiff library to calculate the gradient of a model.

I would now like to do the same in Pyro but am unsure where to begin. I have not found the equivalent `log_density`

method in Pyro and am unsure if this is because I am looking in the wrong place, or because it is not implemented in Pyro.

Any pointers are appreciated!