Not sure why one would use this over Uber’s Pyro, which is built in python on top of Pytorch and has a similar feature set.

Also worth pointing out that, like all current gen PPLs, the word “Universal” is highly misleading, as only variables that are the result of a sample statement can be observed (since HMC, IS, and others require closed form conditional likelihood’s for observed variables). This rules out use cases such as observing noiseless aggregates of noisy signals (any workaround is a fudge and will put strain on the inference algorithms capacity for search).

Only a PPL with support for likelihood free inference techniques (few of which work very well as of today) could be considered truly “Universal”.