Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Einstein VI: General and Integrated Stein Variational Inference in NumPyro
Stein Variational Inference is a technique for approximate Bayesian inferencethat is recently gaining popularity since it combines the scalability of traditionalVariational Inference (VI) with the flexibility of non-para…
Probabilistic ProgrammingVariational InferenceBayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation
Cardinality estimation (CardEst) is an essential component in query optimizers and a fundamental problem in DBMS. A desired CardEst method should attain good algorithm performance, be stable to varied data settings, and …
Probabilistic ProgrammingParaconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation
It is argued that 4-valued paraconsistent truth values (called here "p-bits") can serve as a conceptual, mathematical and practical foundation for highly AI-relevant forms of probabilistic logic and probabilistic program…
Probabilistic ProgrammingSpacecraft Collision Risk Assessment with Probabilistic Programming
Over 34,000 objects bigger than 10 cm in length are known to orbit Earth. Among them, only a small percentage are active satellites, while the rest of the population is made of dead satellites, rocket bodies, and debris …
Bayesian InferenceProbabilistic ProgrammingComplex Coordinate-Based Meta-Analysis with Probabilistic Programming
With the growing number of published functional magnetic resonance imaging (fMRI) studies, meta-analysis databases and models have become an integral part of brain mapping research. Coordinate-based meta-analysis (CBMA) …
Probabilistic ProgrammingTransforming 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 ProgrammingvalidSurvival prediction and risk estimation of Glioma patients using mRNA expressions
Gliomas are lethal type of central nervous system tumors with a poor prognosis. Recently, with the advancements in the micro-array technologies thousands of gene expression related data of glioma patients are acquired, l…
Probabilistic ProgrammingPrognosisSurvival PredictionRecalibrating classifiers for interpretable abusive content detection
We investigate the use of machine learning classifiers for detecting online abuse in empirical research. We show that uncalibrated classifiers (i.e. where the ‘raw’ scores are used) align poorly with human evaluations. T…
Probabilistic ProgrammingBayCANN: Streamlining Bayesian Calibration with Artificial Neural Network Metamodeling
Purpose: Bayesian calibration is theoretically superior to standard direct-search algorithm because it can reveal the full joint posterior distribution of the calibrated parameters. However, to date, Bayesian calibration…
Probabilistic ProgrammingAccelerating Metropolis-Hastings with Lightweight Inference Compilation
In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in an open-source framework we call Lightweight Infere…
Probabilistic ProgrammingConditional independence by typing
A central goal of probabilistic programming languages (PPLs) is to separate modelling from inference. However, this goal is hard to achieve in practice. Users are often forced to re-write their models in order to improve…
Probabilistic ProgrammingFinancial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction
In recent years, bankruptcy forecasting has gained lot of attention from researchers as well as practitioners in the field of financial risk management. For bankruptcy prediction, various approaches proposed in the past …
ManagementMedical DiagnosisPredictionProbabilistic ProgrammingPPL Bench: Evaluation Framework For Probabilistic Programming Languages
We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and evaluation code for a number of models as w…
Probabilistic ProgrammingScenic: A Language for Scenario Specification and Data Generation
We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. Specifically, we consider the problems of training a system to be rob…
Probabilistic ProgrammingSynthetic Data GenerationSimulation-based inference methods for particle physics
Our predictions for particle physics processes are realized in a chain of complex simulators. They allow us to generate high-fidelity simulated data, but they are not well-suited for inference on the theory parameters wi…
Probabilistic ProgrammingSymbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
Probabilistic software analysis aims at quantifying the probability of a target event occurring during the execution of a program processing uncertain incoming data or written itself using probabilistic programming const…
Probabilistic ProgrammingSPPL: Probabilistic Programming with Fast Exact Symbolic Inference
We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of probabilistic inference queries. SPPL translates probabil…
FairnessProbabilistic ProgrammingTranslationProbabilistic Programs with Stochastic Conditioning
We tackle the problem of conditioning probabilistic programs on distributions of observable variables. Probabilistic programs are usually conditioned on samples from the joint data distribution, which we refer to as dete…
Probabilistic ProgrammingBayesian 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 InferenceNeuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels
We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches…
BIG-bench Machine LearningDisease PredictionGaussian ProcessesProbabilistic Programming