Model Informed Flows for Bayesian Inference of Probabilistic Programs
Variational inference often struggles with the posterior geometry exhibited by complex hierarchical Bayesian models. Recent advances in flow-based variational families and Variationally Inferred Parameters (VIP) each address aspects of this challenge, but their formal relationship is unexplored. Here, we prove that the combination of VIP and a full-rank Gaussian can be represented exactly as a forward autoregressive flow augmented with a translation term and input from the model's prior. Guided by this theoretical insight, we introduce the Model-Informed Flow (MIF) architecture, which adds the necessary translation mechanism, prior information, and hierarchical ordering. Empirically, MIF delivers tighter posterior approximations and matches or exceeds state-of-the-art performance across a suite of hierarchical and non-hierarchical benchmarks.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceTranslationVariational InferenceSimilar Papers 제목 키워드 기반
Automatic variational inference with cascading flows
The automation of probabilistic reasoning is one of the primary aims of machine learning. Recently, the confluence of variational inference and deep learning has led to powerful and flexible automatic inference methods t…
Variational InferenceBayesian Policy Search for Stochastic Domains
AI planning can be cast as inference in probabilistic models, and probabilistic programming was shown to be capable of policy search in partially observable domains. Prior work introduces policy search through Markov cha…
Bayesian InferenceProbabilistic ProgrammingVariational InferenceBayesian causal inference via probabilistic program synthesis
Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions. We show that it is possible to implemen…
Bayesian InferenceCausal InferenceProbabilistic ProgrammingProgram SynthesisBayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that cap…
Bayesian InferenceProbabilistic ProgrammingTime SeriesTime Series AnalysisApproximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached …
Probabilistic Programming