Excellent! Always believed being able to "play" with the Hamiltonian particle would be a great motivator in understanding physics ;) This is why I get so excit

Excellent! Always believed being able to "play" with the Hamiltonian particle would be a great motivator in understanding physics ;) This is why I get so excited about about probabilistic programming in general. 1M dimension data sets likelihood estimation in reasonable time right on your laptop. Real world samples are usually sparse, heterogeneous. By abstracting out your analysis it not only reduces the chance for human error. But allows for ingestion of even more disparate sets of archival and de novo data sources. I have little doubt this will lead to more nuanced theories. And higher reproducibility of results. A recent example of the state-of-the-art: predicting rare events in pediatric transplant surgeries. https://arxiv.org/abs/1809.04407 https://arxiv.org/abs/1809.04407