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***: a general-purpose probabilistic programming language

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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The joy of probabilistic Answer Set programming: Semantics, complexity, expressivity, inference

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2020年 125卷 218-239页

作者： Cozman, Fabio Gagliardi Maua, Denis Deratani Univ Sao Paulo Ctr Artificial Intelligence C4AI Sao Paulo Brazil Univ Sao Paulo Escola Politecn Sao Paulo Brazil Univ Sao Paulo Inst Matemat & Estat Sao Paulo Brazil

probabilistic Answer Set programming (PASP) combines rules, facts, and independent probabilistic facts. We argue that a very useful modeling paradigm is obtained by adopting a particular semantics for PASP, where one associates a credal set with each consistent program. We examine the basic properties of PASP under this credal semantics, in particular presenting novel results on its complexity and its expressivity, and we introduce an inference algorithm to compute (upper) probabilities given a program. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Logic programming Answer Set programming probabilistic programming Credal sets Computational complexity Descriptive complexity

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A hybrid probabilistic fuzzy goal programming approach for agricultural decision-making

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2016年 173卷 134-141页

作者： Jana, R. K. Sharma, Dinesh K. Chakraborty, B. Indian Inst Social Welf & Business Management Kolkata India Univ Maryland Eastern Shore Princess Anne MD USA Inst Engn & Management Kolkata India

In this paper, we present a probabilistic fuzzy goal programming model to capture different uncertainties in an agricultural decision-making environment. First, we construct the goals of the model as probabilistic fuzzy goals. Next, we convert the probabilistic fuzzy goal programming problem to a probabilistic constrained programming problem. While a deterministic solution to this problem cannot be derived, we use a hybrid approach comprising Monte-Carlo simulation and a real-coded genetic algorithm. The application of the proposed model and the solution technique is demonstrated by considering the agricultural planning of the Danton-II community development block of Paschim Medinipur District, West Bengal, India. (C) 2015 Elsevier B.V. All rights reserved.

关键词： probabilistic programming probabilistic fuzzy goal programming Real coded genetic algorithm Monte-Carlo simulation Agricultural planning

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probabilistic abductive logic programming using Dirichlet priors

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2016年 78卷 223-240页

作者： Turliuc, Calin Rares Dickens, Luke Russo, Alessandra Broda, Krysia Imperial Coll London Dept Comp London England UCL Dept Informat Studies London WC1E 6BT England

probabilistic programming is an area of research that aims to develop general inference algorithms for probabilistic models expressed as probabilistic programs whose execution corresponds to inferring the parameters of those models. In this paper, we introduce a probabilistic programming language (PPL) based on abductive logic programming for performing inference in probabilistic models involving categorical distributions with Dirichlet priors. We encode these models as abductive logic programs enriched with probabilistic definitions and queries, and show how to execute and compile them to boolean formulas. Using the latter, we perform generalized inference using one of two proposed Markov Chain Monte Carlo (MCMC) sampling algorithms: an adaptation of uncollapsed Gibbs sampling from related work and a novel collapsed Gibbs sampling (CGS). We show that CGS converges faster than the uncollapsed version on a latent Dirichlet allocation (LDA) task using synthetic data. On similar data, we compare our PPL with LDA-specific algorithms and other PPLs. We find that all methods, except one, perform similarly and that the more expressive the PPL, the slower it is. We illustrate applications of our PPL on real data in two variants of LDA models (Seed and Cluster LDA), and in the repeated insertion model (RIM). In the latter, our PPL yields similar conclusions to inference with EM for Mallows models. (C) 2016 The Author(s). Published by Elsevier Inc.

关键词： probabilistic programming Abductive logic programming Markov Chain Monte Carlo Latent Dirichlet allocation Repeated insertion model

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Compositional Imprecise Probability A Solution from Graded Monads and Markov Categories

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2025年第POPL期9卷 1596-1626页

作者： Liell-cock, Jack Staton, Sam Univ Oxford Oxford England

Imprecise probability is concerned with uncertainty about which probability distributions to use. It has applications in robust statistics and machine learning. We look at programming language models for imprecise probability. Our desiderata are that we would like our model to support all kinds of composition, categorical and monoidal;in other words, guided by dataflow diagrams. Another equivalent perspective is that we would like a model of synthetic probability in the sense of Markov categories. Imprecise probability can be modelled in various ways, with the leading monad-based approach using convex sets of probability distributions. This model is not fully compositional because the monad involved is not commutative, meaning it does not have a proper monoidal structure. In this work, we provide a new fully compositional account. The key idea is to name the non-deterministic choices. To manage the renamings and disjointness of names, we use graded monads. We show that the resulting compositional model is maximal and relate it with the earlier monadic approach, proving that we obtain tighter bounds on the uncertainty.

关键词： imprecise probability probabilistic programming graded monads

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A Demonic Outcome Logic for Randomized Nondeterminism

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2025年第POPL期9卷 539-568页

作者： Zilberstein, Noam Kozen, Dexter Silva, Alexandra Tassarotti, Joseph Cornell Univ Ithaca NY 14850 USA NYU New York NY USA

Programs increasingly rely on randomization in applications such as cryptography and machine learning. Analyzing randomized programs has been a fruitful research direction, but there is a gap when programs also exploit nondeterminism (for concurrency, efficiency, or algorithmic design). In this paper, we introduce Demonic Outcome Logic for reasoning about programs that exploit both randomization and nondeterminism. The logic includes several novel features, such as reasoning about multiple executions in tandem and manipulating pre- and postconditions using familiar equational laws-including the distributive law of probabilistic choices over nondeterministic ones. We also give rules for loops that both establish termination and quantify the distribution of final outcomes from a single premise. We illustrate the reasoning capabilities of Demonic Outcome Logic through several case studies, including the Monty Hall problem, an adversarial protocol for simulating fair coins, and a heuristic based probabilistic SAT solver.

关键词： Program Logics probabilistic programming Demonic Nondeterminism

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Guaranteed Bounds on Posterior Distributions of Discrete probabilistic Programs with Loops

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2025年第POPL期9卷 1104-1135页

作者： Zaiser, Fabian Murawski, Andrzej S. Ong, C. -H. Luke Univ Oxford Oxford England Nanyang Technol Univ Singapore Singapore

We study the problem of bounding the posterior distribution of discrete probabilistic programs with unbounded support, loops, and conditioning. Loops pose the main difficulty in this setting: even if exact Bayesian inference is possible, the state of the art requires user-provided loop invariant templates. By contrast, we aim to find guaranteed bounds, which sandwich the true distribution. They are fully automated, applicable to more programs and provide more provable guarantees than approximate sampling-based inference. Since lower bounds can be obtained by unrolling loops, the main challenge is upper bounds, and we attack it in two ways. The first is called residual mass semantics, which is a flat bound based on the residual probability mass of a loop. The approach is simple, efficient, and has provable guarantees. The main novelty of our work is the second approach, called geometric bound semantics. It operates on a novel family of distributions, called eventually geometric distributions (EGDs), and can bound the distribution of loops with a new form of loop invariants called contraction invariants. The invariant synthesis problem reduces to a system of polynomial inequality constraints, which is a decidable problem with automated solvers. If a solution exists, it yields an exponentially decreasing bound on the whole distribution, and can therefore bound moments and tail asymptotics as well, not just probabilities as in the first approach. Both semantics enjoy desirable theoretical properties. In particular, we prove soundness and convergence, i.e. the bounds converge to the exact posterior as loops are unrolled further. We also investigate sufficient and necessary conditions for the existence of geometric bounds. On the practical side, we describe Diabolo, a fully-automated implementation of both semantics, and evaluate them on a variety of benchmarks from the literature, demonstrating their general applicability and the utility of the resulting bounds.

关键词： probabilistic programming Bayesian inference program analysis guaranteed bounds posterior distribution moments tail asymptotics loop invariant synthesis quantitative analysis

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Genetic programming on Program Traces as an Inference Engine for probabilistic Languages 8th

Genetic Programming on Program Traces as an Inference Engine...

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8th International Conference on Artificial General Intelligence (AGI)

作者： Batishcheva, Vita Potapov, Alexey ITMO Univ St Petersburg Russia St Petersburg State Univ St Petersburg 199034 Russia

ISBN: (纸本)9783319213651;9783319213644

Methods of simulated annealing and genetic programming over probabilistic program traces are developed firstly. These methods combine expressiveness of Turing-complete probabilistic languages, in which arbitrary generative models can be defined, and search effectiveness of meta-heuristic methods. To use these methods, one should only specify a generative model of objects of interest and a fitness function over them without necessity to implement domain-specific genetic operators or mappings from objects to and from bit strings. On the other hand, implemented methods showed better quality than the traditional mh-query on several optimization tasks. Thus, these results can contribute to both fields of genetic programming and probabilistic programming.

关键词： probabilistic programming Genetic programming Program traces

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The Joy of probabilistic Answer Set programming 11

The Joy of Probabilistic Answer Set Programming

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11th International Symposium on Imprecise Probabilities - Theories and Applications (ISIPTA)

作者： Cozman, Fabio Gagliardi Univ Sao Paulo Escola Politecn Sao Paulo Brazil

probabilistic answer set programming (PASP) combines rules, facts, and independent probabilistic facts. Often one restricts such programs so that every query yields a sharp probability value. The purpose of this paper is to argue that a very useful modeling language is obtained by adopting a particular credal semantics for PASP, where one associates with each consistent program a credal set. We examine the basic properties of PASP and present an algorithm to compute (upper) probabilities given a program.

关键词： Logic programming Answer set programming probabilistic programming Credal sets

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Quantum programming Without the Quantum Physics 22nd

Quantum Programming Without the Quantum Physics

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22nd Asian Symposium on programming Languages and Systems

作者： Inoue, Jun Natl Inst Adv Ind Sci & Technol 1-8-31 Midorigaoka Ikeda Osaka Japan

ISBN: (纸本)9789819789429;9789819789436

We propose a quantum programming paradigm where all data are familiar classical data, and the only non-classical element is a random number generator that can return results with negative probability. Currently, the vast majority of quantum programming languages instead work with quantum data types made up of qubits. The description of their behavior relies on heavy linear algebra and many interdependent concepts and intuitions from quantum physics, which takes dedicated study to understand. We demonstrate that the proposed view of quantum programming explains its central concepts and constraints in more accessible, computationally relevant terms. This is achieved by systematically reducing everything to the existence of that negative-probability random generator, avoiding mention of advanced physics. This makes quantum programming more accessible to programmers without a deep background in physics or linear algebra. The bulk of this paper is written with such an audience in mind. As a working vehicle, we lay out a simple quantum programming language under this paradigm, showing that not only can it express all quantum algorithms, it also naturally captures the semantics of measurement without ever mentioning qubits or collapse.

关键词： Quantum programming probabilistic programming programming model

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