4 papers · 1 filter
Stacking-Based Weighting for Large Language Bayes in M-Open Settings
Anant Bhide, Akarsh Gupta, Edmond Cunningham +1
Large Language Bayes (LLB) transforms a natural language problem description into a collection of candidate probabilistic programs and performs inference within each model, combini…
Amortized Factor Inference Networks for Posterior Inference
Joohwan Ko, Justin Domke
Amortized inference promises fast test-time Bayesian inference, but existing methods typically target a fixed model or a restricted model family. Extending amortization to new mode…
Model-Informed Flows for Bayesian Inference
Joohwan Ko, Justin Domke
Variational inference often struggles with the posterior geometry exhibited by complex hierarchical Bayesian models. Recent advances in flow-based variational families and Variatio…
Large Language Bayes
Justin Domke
Many domain experts do not have the time or expertise to write formal Bayesian models. This paper takes an informal problem description as input, and combines a large language mode…