[cshenton]

Bayesian statistics gets tonnes of practical use. Software packages like STAN-MC get lots of use in traditional stats/econometric circles, while probabilistic programming languages like Turing, Edward, and PyMC get plenty of use elsewhere. Developing algorithms to sample from or solve for the posterior distribution given an arbitrary prior and likelihood is an active area of research. If you want stuff to google, relevant families of algorithms are Variational Inference, Approximate Bayesian Computation, Markov Chain Monte Carlo, and Particle Filters / SMC. However there’s a few newer families of algorithms that have popped up in recent years.

[cshenton]

Also worth pointing out that Bayesian methods are the only good option when performing data assimilation into large scale simulators (like epidemic models) which are typically statistically under identified by available data. So this stuff is very relevant!