Faster Bayesian inference with Pathfinder

Let me tell you about the most frustrating part of Bayesian modeling. Often the first models you build either make bad assumptions or contain bugs. Both can cause the already expensive step of drawing posterior samples using Markov Chain Monte Carlo (MCMC) to become unbearably slow, but those samples are often the best way to check if our model makes sense. So we draw samples, encode some more better assumptions, draw more samples, fix some bugs, draw more samples, go check our error model against the laboratory equipment, rinse and repeat. And gradually we move toward higher quality, more useful models, which often can be sampled much faster. This is known as the folk theorem of statistical computing.