PyMC Labs on amortized Bayesian inference: train once, get posteriors in milliseconds
twiecki · x · 2026-11-06
PyMC Labs published a new blog on amortized Bayesian inference: instead of paying full MCMC sampling cost for every fit, you train a network once on simulations and then get posteriors from a single forward pass. Aimed at workflows that fit the same model hundreds of times, where per-fit MCMC cost scales linearly with the number of fits.
More from Research
- ICLR reviews starting to leak, authors still waiting on scores — Invariant_n_Cauchy · 2026-11-06
- Researcher defends AI-based ranking of 1660 STOC submissions after backlash — vitalyFM · 2026-11-06
- Stanford launches Collaborative Intelligence project on human-agent teamwork for future of work — Diyi_Yang · 2026-11-06
- Chai Discovery launches, open-sources Chai-1, lands deals with seven big pharma firms — chaitjo · 2026-11-06
- Johns Hopkins study compares four failure data sources for robot reward models — mangahomanga · 2026-11-06
- DFlash 2 parallel drafting lifts speculative decoding acceptance 21% for 1.3% latency cost — songhan_mit · 2026-11-06