Automatically Marginalized MCMC in Probabilistic Programming
Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models. The advent of probabilistic programming languages (PPLs) frees users from writing inference algorithms and lets users focus on modeling. However, many models are difficult for HMC to solve directly, and often require tricks like model reparameterization. We are motivated by the fact that many of those models could be simplified by marginalization. We propose to use automatic marginalization as part of the sampling process using HMC in a graphical model extracted from a PPL, which substantially improves sampling from real-world hierarchical models.
Code (1)
Tasks
Probabilistic ProgrammingSimilar Papers 제목 키워드 기반
Parameter elimination in particle Gibbs sampling
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories. A viable approach is particle Markov chain Monte Carlo, combining MCMC and sequential Monte Carlo to form "exact approxi…
Bayesian InferenceEpidemiologyProbabilistic ProgrammingState Space ModelsTransforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models
Open-universe probabilistic models enable Bayesian inference about how many objects underlie data, and how they are related. Effective inference in OUPMs remains a challenge, however, often requiring the use of custom, t…
Bayesian InferenceProbabilistic ProgrammingvalidDesigning Perceptual Puzzles by Differentiating Probabilistic Programs
We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search effic…
Bayesian InferenceColor ConstancyProbabilistic ProgrammingHamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
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 to write the model in a probabilistic pro…
Probabilistic ProgrammingComposing inference algorithms as program transformations
Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm. We reduce this effort by generating inference procedures from models automatically. We …
Code GenerationProbabilistic Programming