Can Neural Networks Replace MCMC?
Notes de l’épisode
Today’s clip is from Episode 167 of Learning Bayesian Statistics, featuring Paul-Christian Bürkner and Steve Bronder, recorded live at StanCon 2026 in Uppsala, Sweden.
Paul Bürkner, creator of brms and a leading researcher behind BayesFlow, explains how neural networks can replace MCMC for Bayesian inference. Instead of evaluating likelihood and prior densities, the networks are trained on simulated data, so you can do simulation-based inference (SBI) even when the likelihood is intractable. Once trained, they give you amortized, real-time posteriors for new datasets, and they have made SBI scalable well beyond what approximate Bayesian computation could handle.
Paul is also candid about the limits. Neural methods come with unstable training and no guarantees, and they can be unreliable on out-of-distribution or misspecified data, which is why MCMC is still the more trustworthy option wherever you can apply it. He ends with how his group is building diagnostics to flag when a neural posterior can’t be trusted.
Full discussion here
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