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cs.LG2024
Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI
Abhinav Agrawal, Justin Domke
Normalizing flow-based variational inference (flow VI) is a promising approximate inference approach, but its performance remains inconsistent across studies. Numerous algorithmic…
cs.LG2024
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
Jinlin Lai, Justin Domke, Daniel Sheldon
Bayesian reasoning in linear mixed-effects models (LMMs) is challenging and often requires advanced sampling techniques like Markov chain Monte Carlo (MCMC). A common approach is t…
cs.LG2024
Understanding and mitigating difficulties in posterior predictive evaluation
Abhinav Agrawal, Justin Domke
Predictive posterior densities (PPDs) are of interest in approximate Bayesian inference. Typically, these are estimated by simple Monte Carlo (MC) averages using samples from the a…