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!