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Pyro: Deep Universal probabilistic programming

JOURNAL OF MACHINE LEARNING RESEARCH 2019年 20卷

作者： Bingham, Eli Chen, Jonathan P. Jankowiak, Martin Obermeyer, Fritz Pradhan, Neeraj Karaletsos, Theofanis Singh, Rohit Szerlip, Paul Horsfall, Paul Goodman, Noah D. Uber Technol Inc Uber AI Labs 555 Market St San Francisco CA 94103 USA Uber AI Labs 555 Market St San Francisco CA 94103 USA Stanford Univ Stanford CA 94305 USA

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.

关键词： probabilistic programming graphical models approximate Bayesian inference generative models deep learning

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StarfishDB: A Query Execution Engine for Relational probabilistic programming

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Proceedings of the ACM on Management of Data 2024年第3期2卷 1-31页

作者： Ouael Ben Amara Sami Hadouaj Niccolò Meneghetti Computer and Information Science (CIS) University of Michigan-Dearborn Dearborn MI USA

We introduce StarfishDB, a query execution engine optimized for relational probabilistic programming. Our engine adopts the model of Gamma probabilistic Databases, representing probabilistic programs as a collection of relational constraints, imposed against a generative stochastic process. We extend the model with the support for recursion, factorization and the ability to leverage just-in-time compilation techniques to speed up inference. We test our engine against a state-of-the-art sampler for Latent Dirichlet Allocation.

关键词： Bayesian inference probabilistic databases probabilistic programming

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Note on Solving probabilistic programming Problems Involving Multi-choice Parameters

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2015年第5期18卷 617-627页

作者： Hung, Kuo-Chen Lin, Scott Shu-Cheng Chu, Chun-Hsiao Hungkuang Univ Dept Comp Sci & Informat Management Taichung Taiwan Chungyu Inst Technol Dept Tourism & Travel Management Keelung Taiwan Aletheia Univ Dept Tourism New Taipei Taiwan

We study the probabilistic programming problems with multi-choice parameters proposed by Acharya and Biswal (2011) that was published in Opsearch. We point out that the multi-choice parameters can be simplified to avoid the complicated solution procedure proposed by Acharya and Biswal (2011). We use the same numerical example of Acharya and Biswal (2011) to demonstrate that our simplification is effective to derive better minimum.

关键词： probabilistic programming multi-choice programming non-linear programming deterministic programming

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probabilistic logic programming for hybrid relational domains

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MACHINE LEARNING 2016年第3期103卷 407-449页

作者： Nitti, Davide De Laet, Tinne De Raedt, Luc Katholieke Univ Leuven Dept Comp Sci Leuven Belgium Katholieke Univ Leuven Fac Engn Sci Leuven Belgium

We introduce a probabilistic language and an efficient inference algorithm based on distributional clauses for static and dynamic inference in hybrid relational domains. Static inference is based on sampling, where the samples represent (partial) worlds (with discrete and continuous variables). Furthermore, we use backward reasoning to determine which facts should be included in the partial worlds. For filtering in dynamic models we combine the static inference algorithm with particle filters and guarantee that the previous partial samples can be safely forgotten, a condition that does not hold in most logical filtering frameworks. Experiments show that the proposed framework can outperform classic sampling methods for static and dynamic inference and that it is promising for robotics and vision applications. In addition, it provides the correct results in domains in which most probabilistic programming languages fail.

关键词： probabilistic programming Statistical relational learning Discrete and continuous distributions Particle filter Likelihood weighting Logic programming

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Exploring Bayesian Approaches to eQTL Mapping Through probabilistic programming

Exploring Bayesian Approaches to eQTL Mapping Through Probab...

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作者： Dimitrios V. Vavoulis

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 aspect in the development of such models is the implementation of bespoke inference methodologies, a process which can become quite laborious, when multiple candidate models are being considered. We describe automatic, black-box inference in such models using Stan, a popular probabilistic programming language. The utilization of systems like Stan can facilitate model prototyping and testing, thus accelerating the data modeling process. The code described in this chapter can be found at https://***/dvav/eQTLBookChapter. less

关键词： RNA-seq Statistical Calculation programming Language R Stan eQTL mapping Bayesian variable selection Global-local shrinkage Horseshoe prior probabilistic programming Black-box Bayesian inference

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A probabilistic programming Approach for Outlier Detection in Healthcare Claims

A Probabilistic Programming Approach for Outlier Detection i...

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IEEE International Conference on Machine Learning and Applications

作者： Richard A. Bauder Taghi M. Khoshgoftaar Florida Atlantic University

ISBN: (纸本)9781509061686

Healthcare is an integral component in people's lives, especially for the rising elderly population. Medicare is one such healthcare program that provides for the needs of the elderly. It is imperative that these healthcare programs are affordable, but this is not always the case. Out of the many possible factors for the rising cost of healthcare, claims fraud is a major contributor, but its impact can be lessened through effective fraud detection. We propose a general outlier detection model, based on Bayesian inference, using probabilistic programming. Our model provides probability distributions rather than just point values, as with most common outlier detection methods. Credible intervals are also generated to further enhance confidence that the detected outliers should in fact be considered outliers. Two case studies are presented demonstrating our model's effectiveness in detecting outliers. The first case study uses temperature data in order to provide a clear comparison of several outlier detection techniques. The second case study uses a Medicare dataset to showcase our proposed outlier detection model. Our results show that the successful detection of outliers, which indicate possible fraudulent activities, can provide effective and meaningful results for further investigation within medical specialties or by using real-world, medical provider fraud investigation cases.

关键词： Fraud Detection Outlier Detection Healthcare Fraud Bayesian Inference probabilistic programming

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ProbLog2: probabilistic Logic programming

ProbLog2: Probabilistic Logic Programming

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Dries, Anton Kimmig, Angelika Meert, Wannes Renkens, Joris Van den Broeck, Guy Vlasselaer, Jonas De Raedt, Luc Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9783319234618;9783319234601

We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for querying such models as well as for learning their parameters from data. It is available as an online tool on the web and for download. The offline version offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the system's components.

关键词： probabilistic programming probabilistic inference Parameter learning

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Multi-choice probabilistic linear programming problem

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OPSEARCH 2017年第1期54卷 122-142页

作者： Pradhan, Avik Biswal, M. P. Indian Inst Technol Kharagpur Dept Math Kharagpur 721302 W Bengal India

In this paper, we present a linear programming model where the parameter space contains some multi-choice parameters. Alternative choices of multi-choice parameter are considered as random variables. Using interpolating polynomial for each multi-choice parameter, the model has been transformed into a non-linear mixed integer probabilistic programming problem. Then chance constrained programming technique is used to obtain an equivalent deterministic model of the transformed problem. To find the deterministic form of the objective function four different models namely, E-model, V-model, probability maximization model and fractile criterion model are used. Assuming the values of the multi-choice parameters as independent normal random variables, the methodology is presented. A numerical example is also presented to illustrate the methodology.

关键词： Chance-constrained programming Multi-choice programming Linear programming probabilistic programming

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Declarative probabilistic logic programming in discrete-continuous domains

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ARTIFICIAL INTELLIGENCE 2024年 337卷

作者： Dos Martires, Pedro Zuidberg De Raedt, Luc Kimmig, Angelika Orebro Univ Ctr Appl Autonomous Sensor Syst Orebro Sweden Katholieke Univ Leuven Dept Comp Sci Leuven Belgium Leuven AI Leuven Belgium

Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and its programming languages owes much of its success to a declarative semantics, the so-called distribution semantics. However, the distribution semantics is limited to discrete random variables only. While PLP has been extended in various ways for supporting hybrid, that is, mixed discrete and continuous random variables, we are still lacking a declarative semantics for hybrid PLP that not only generalizes the distribution semantics and the modeling language but also the standard inference algorithm that is based on knowledge compilation. We contribute the measure semantics together with the hybrid PLP language DC-ProbLog (where DC stands for distributional clauses) and its inference engine infinitesimal algebraic likelihood weighting (IALW). These have the original distribution semantics, standard PLP languages such as ProbLog, and standard inference engines for PLP based on knowledge compilation as special cases. Thus, we generalize the state of the art of PLP towards hybrid PLP in three different aspects: semantics, language and inference. Furthermore, IALW is the first inference algorithm for hybrid probabilistic programming based on knowledge compilation.

关键词： probabilistic programming Declarative semantics Discrete-continuous distributions Likelihood weighting Logic programming Knowledge compilation Algebraic model counting

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***: a general-purpose probabilistic programming language

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ACM Transactions on probabilistic Machine Learning 1000年

作者： Tor Erlend Fjelde Kai Xu David Widmann Mohamed Tarek Cameron Pfiffer Martin Trapp Seth D. Axen Xianda Sun Markus Hauru Penelope Yong Will Tebbutt Zoubin Ghahramani Hong Ge University of Cambridge United Kingdom MIT-IBM Watson AI Lab USA Pumas-AI Inc. USA Pumas-AI Inc. USA and Business School University of Sydney Australia Stanford University USA Aalto University Finland University of Tübingen Germany The Alan Turing Institute United Kingdom Google DeepMind and University of Cambridge United Kingdom

probabilistic programming languages (PPLs) are becoming increasingly important in many scientific disciplines, such as economics, epidemiology, and biology, to extract meaning from sources of data while accounting for one's uncertainty. The key idea of probabilistic programming is to decouple inference and model specification, thus allowing the practitioner to approach their task at hand using Bayesian inference, without requiring extensive knowledge in programming or computational statistics. At the same time, the complexity of problem settings in which PPLs are employed is steadily increasing, both in terms of project size and model complexity, calling for more flexible and efficient *** this work, we describe ***, a general-purpose PPL, which is designed to be flexible, efficient, and easy to use. *** is built on top of the Julia programming language, which is known for its high performance and ease-of-use. We describe the design of ***, contextualizing it within different types of users and use cases, its key features, and how it can be used to solve a wide range of problems. We also provide a brief overview of the ecosystem around ***, including the different libraries and tools that can be used in conjunction with it. Finally, we provide a few examples of how *** can be used in practice.

关键词： probabilistic programming probabilistic programming Languages probabilistic Inference Bayesian Inference Markov Chain Monte Carlo Variational Inference Sequential Monte Carlo Uncertainty Quantification Modeling Methodologies Latent Variable Models Maximum a Posteriori Modeling Software Libraries and Repositories Bayesian Computation Variational Methods Sequential Monte Carlo Methods

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