Machine Learning and the Future of Bayesian Computation
This is an incremental discussion on enhancing Bayesian computation for researchers in statistics and machine learning.
The paper addresses the computational infeasibility of MCMC for high-dimensional Bayesian models by exploring machine learning techniques like normalizing flows and variational inference to improve posterior computation.
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high dimensional models with many observations. In this article we discuss the potential to improve posterior computation using ideas from machine learning. Concrete future directions are explored in vignettes on normalizing flows, Bayesian coresets, distributed Bayesian inference, and variational inference.