Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Bayesian 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 SynthesisDivide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models. They further attempt to automate the process of drawing inferences from these models, but doing th…
Probabilistic ProgrammingProbabilistic Surrogate Networks for Simulators with Unbounded Randomness
We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators. Unlike traditional approaches to surrogate modeling, our surrogates retain the interpr…
Probabilistic ProgrammingProgram SynthesisFunctional Tensors for Probabilistic Programming
It is a significant challenge to design probabilistic programming systems that can accommodate a wide variety of inference strategies within a unified framework. Noting that the versatility of modern automatic differenti…
Probabilistic ProgrammingAmortized Rejection Sampling in Universal Probabilistic Programming
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. This is particularly true of importance sampling inference in programs that explicitly…
Probabilistic ProgrammingMultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the c…
counterfactualCounterfactual InferenceProbabilistic ProgrammingUniversal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty. Inference methods used in PPL can be computational…
Probabilistic ProgrammingEfficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows
We introduce a more efficient neural architecture for amortized inference, which combines continuous and conditional normalizing flows using a principled choice of structure. Our gradient flow derives its sparsity patter…
Probabilistic ProgrammingGraph Tracking in Dynamic Probabilistic Programs via Source Transformations
Many modern machine learning algorithms, such as automatic differentiation (AD) and versions of approximate Bayesian inference, can be understood as a particular case of message passing on some computation graph. To perm…
Bayesian InferenceBIG-bench Machine LearningProbabilistic ProgrammingStatic Analysis for Probabilistic Programs
Probabilistic programming is a powerful abstraction for statistical machine learning. Applying static analysis methods to probabilistic programs could serve to optimize the learning process, automatically verify properti…
BIG-bench Machine LearningProbabilistic ProgrammingStrengthening the Case for a Bayesian Approach to Car-following Model Calibration and Validation using Probabilistic Programming
Compute and memory constraints have historically prevented traffic simulation software users from fully utilizing the predictive models underlying them. When calibrating car-following models, particularly, accommodations…
Probabilistic ProgrammingTowards Verified Stochastic Variational Inference for Probabilistic Programs
Probabilistic programming is the idea of writing models from statistics and machine learning using program notations and reasoning about these models using generic inference engines. Recently its combination with deep le…
Probabilistic ProgrammingVariational InferenceBayesian 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 AnalysisProbabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle filter (APF) with automatic Rao-Blackwelli…
Probabilistic ProgrammingEtalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remain limited because of the impracticabilit…
Bayesian InferenceCPUProbabilistic ProgrammingBayesian deep learning with hierarchical prior: Predictions from limited and noisy data
Datasets in engineering applications are often limited and contaminated, mainly due to unavoidable measurement noise and signal distortion. Thus, using conventional data-driven approaches to build a reliable discriminati…
Bayesian InferenceProbabilistic ProgrammingVariational InferenceDeployable probabilistic programming
We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming fa…
Decision MakingProbabilistic ProgrammingExploring Bayesian approaches to eQTL mapping through probabilistic programming
The discovery of genomic polymorphisms influencing gene expression (also known as expression quantitative trait loci or eQTLs) can be formulated as a sparse Bayesian multivariate/multiple regression problem. An important…
Probabilistic ProgrammingregressionAutomatic Reparameterisation of Probabilistic Programs
Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow modellers to directly reason about the proce…
Probabilistic ProgrammingHijacking Malaria Simulators with Probabilistic Programming
Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria. However, the complicated and stochastic nature of these simulators can mean…
EpidemiologyProbabilistic Programming