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probabilistic programming with Densities in SlicStan: Efficient, Flexible, and Deterministic

PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2019年第POPL期3卷 1-30页

作者： Gorinova, Maria, I Gordon, Andrew D. Sutton, Charles Univ Edinburgh Edinburgh Midlothian Scotland Microsoft Res Cambridge Cambridge England Google Brain Mountain View CA USA

Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifices some of its usability by adopting a block syntax, which lacks compositionality and flexible user-defined functions. Moreover, the semantics of the language has been mainly given in terms of intuition about implementation, and has not been formalised. This paper provides a formal treatment of the Stan language, and introduces the probabilistic programming language SlicStan - a compositional, self-optimising version of Stan. Our main contributions are (1) the formalisation of a core subset of Stan through an operational density-based semantics;(2) the design and semantics of the Stan-like language SlicStan, which facilities better code reuse and abstraction through its compositional syntax, more flexible functions, and information-flow type system;and (3) a formal, semantic-preserving procedure for translating SlicStan to Stan.

关键词： probabilistic programming information flow analysis

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probabilistic programming Semantics for Name Generation

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PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2021年第POPL期5卷 1–29页

作者： Sabok, Marcin Staton, Sam Stein, Dario Wolman, Michael McGill Univ Dept Math & Stat Montreal PQ Canada Univ Oxford Dept Comp Sci Oxford England

We make a formal analogy between random sampling and fresh name generation. We show that quasi-Borel spaces, a model for probabilistic programming, can soundly interpret the nu-calculus, a calculus for name generation. Moreover, we prove that this semantics is fully abstract up to first-order types. This is surprising for an 'off-the-shelf' model, and requires a novel analysis of probability distributions on function spaces. Our tools are diverse and include descriptive set theory and normal forms for the nu-calculus.

关键词： probabilistic programming name generation nu-calculus quasi-Borel spaces standard Borel spaces descriptive set theory Borel on Borel denotational semantics synthetic probability theory

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probabilistic programming with Programmable Inference 2018

Probabilistic Programming with Programmable Inference

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39th ACM SIGPLAN Conference on programming Language Design and Implementation (PLDI)

作者： Mansinghka, Vikash K. Schaechtle, Ulrich Handa, Shivam Radul, Alexey Chen, Yutian Rinard, Martin MIT Cambridge MA 02139 USA Google DeepMind London England

ISBN: (纸本)9781450356985

We introduce inference metaprogramming for probabilistic programming languages, including new language constructs, a formalism, and the first demonstration of effectiveness in practice. Instead of relying on rigid black-box inference algorithms hard-coded into the language implementation as in previous probabilistic programming languages, inference metaprogramming enables developers to 1) dynamically decompose inference problems into subproblems, 2) apply inference tactics to subproblems, 3) alternate between incorporating new data and performing inference over existing data, and 4) explore multiple execution traces of the probabilistic program at once. Implemented tactics include gradient-based optimization, Markov chain Monte Carlo, variational inference, and sequental Monte Carlo techniques. Inference metaprogramming enables the concise expression of probabilistic models and inference algorithms across diverse fields, such as computer vision, data science, and robotics, within a single probabilistic programming language.

关键词： probabilistic programming Semantics Inference

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probabilistic programming for Java using Symbolic Execution and Model Counting 23

Probabilistic Programming for Java using Symbolic Execution ...

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Conference of South-African-Institute-of-Computer-Scientists-and-Information-Technologists (SAICSIT)

作者： Visser, Willem Pasareanu, Corina S. Stellenbosch Univ Stellenbosch South Africa CMU West Mountain View CA USA NASA Ames Res Ctr Mountain View CA USA

ISBN: (纸本)9781450352505

In this paper we describe a probabilistic programming environment for Java that is based on symbolic execution and model counting. The novelty of the framework is that the probability distributions in the program can themselves be symbolic, which allows parametric probabilistic programming. The framework handles typical probabilistic programming features, such as observe statements, and can be used for the encoding and analysis of Discrete Time Markov Chains (DTMC), Bayesian Networks, etc. We show two examples of using the system: (1) analysis of bubble sort when using an unreliable comparison operation, and, (2) analysis of a simulation model of autonomous aircraft towing vehicles, to show whether plans generated for these vehicles are robust when probability distributions are changed from the ones used to generate the plans.

关键词： probabilistic programming Symbolic Execution Model Counting

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probabilistic programming in Anglican

Probabilistic Programming in Anglican

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

作者： Tolpin, David van de Meent, Jan-Willem Wood, Frank Univ Oxford Dept Engn Sci Oxford England

Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We describe the implementation of Anglican and illustrate how its design facilitates both explorative and i... 详细信息

ISBN: (纸本)9783319234618;9783319234601

关键词： probabilistic programming

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probabilistic programming for assessment of capability and capacity

Probabilistic programming for assessment of capability and c...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XX

作者： Pfeffer, Avi P. Harrison, Scott A. Charles River Analyt Inc Cambridge MA 02138 USA

ISBN: (纸本)9780819486240

Answering the questions "What can the adversary do?" and "What will the adversary do?" are critical functions of intelligence analysis. These questions require processing many sources of information, which is currently performed manually by analysts, leading to missed opportunities and potential mistakes. We have developed a system for Assessment of Capability and Capacity via Intelligence Analysis (ACACIA) to help analysts assess the capability, capacity, and intention of a nation state or non-state actor. ACACIA constructs a Bayesian network (BN) to model the objectives and means of an actor in a situation. However, a straightforward BN implementation is insufficient, since objectives and means are different in every situation. Additionally, we wish to apply knowledge about an element gained from one situation to another situation containing the same element. Furthermore, different elements of the same kind usually share the same model structure with different parameters. We use the probabilistic programming language Figaro, which allows models to be constructed using the power of programming languages, to address these issues, generating BNs for diverse situations while maximizing sharing. We learn the parameters of a program from training instances. Experiments show ACACIA is capable of making accurate inferences and that learning effectively improves ACACIA's performance.

关键词： Intelligence analysis capability and capacity decision support system probabilistic reasoning Bayesian networks probabilistic programming machine learning

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probabilistic programming Inference via Intensional Semantics 28th

Probabilistic Programming Inference via Intensional Semantic...

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28th European Symposium on programming (ESOP) Held as Part of the European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Castellan, Simon Paquet, Hugo Imperial Coll London London England Univ Cambridge Cambridge England

ISBN: (纸本)9783030171841;9783030171834

We define a new denotational semantics for a first-order probabilistic programming language in terms of probabilistic event structures. This semantics is intensional, meaning that the interpretation of a program contains information about its behaviour throughout execution, rather than a simple distribution on return values. In particular, occurrences of sampling and conditioning are recorded as explicit events, partially ordered according to the data dependencies between the corresponding statements in the program. This interpretation is adequate: we show that the usual measure-theoretic semantics of a program can be recovered from its event structure representation. Moreover it can be leveraged for MCMC inference: we prove correct a version of single-site Metropolis-Hastings with incremental recomputation, in which the proposal kernel takes into account the semantic information in order to avoid performing some of the redundant sampling.

关键词： probabilistic programming Denotational semantics Event structures Bayesian inference

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Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第3期16卷 895-908页

作者： Hongbo Zhao Shaojun Li Xiaoyu Zang Xinyi Liu Lin Zhang Jiaolong Ren School of Civil Engineering and Geomatics Shandong University of TechnologyZibo255000China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan430071China

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical *** inverse analysis is commonly utilized to determine the physico-mechanical ***,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological *** this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic *** ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical *** programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical *** results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under ***,a slope case was employed to demonstrate the performance of the developed *** results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

关键词： Geological engineering Geotechnical engineering Inverse analysis Uncertainty quantification probabilistic programming

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PRISM revisited: Declarative implementation of a probabilistic programming language using multi-prompt delimited control

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2018年 103卷 364-382页

作者： Abdallah, Samer Jukedeck Ltd 79-81 Borough Rd London SE1 1DN England

PRISM is a probabilistic programming language based on Prolog, augmented with primitives to represent probabilistic choice. It is implemented using a combination of low level support from a modified version of B-Prolog, source level program transformation, and libraries for inference and learning implemented in C. More recently, developers working with functional programming languages have taken the approach of embedding probabilistic primitives into an existing language, with little or no modification to the host language, often by using delimited continuations. Captured continuations represent pieces of the probabilistic program which can be manipulated to achieve a great variety of computational effects useful for inference. In this paper, I will describe an approach based on delimited control operators recently introduced into SWI Prolog. These are used to create a system of nested effect handlers which together implement a core functionality of PRISM the building of explanation graphs entirely in Prolog and using an order of magnitude less code. Other declarative programming tools, such as constraint logic programming, are used to implement tools for inference, such as the inside-outside and EM algorithms, lazy best-first explanation search, and MCMC samplers. By embedding the functionality of PRISM into SWI Prolog, users gain access to its rich libraries and development environment. By expressing the functionality of PRISM in a small amount of pure, high-level Prolog, this implementation facilitates further experimentation with the mechanisms of probabilistic logic programming, including new probabilistic modelling features and inference algorithms, such as variational inference in models with real-valued variables. (C) 2018 Elsevier Inc. All rights reserved.

关键词： probabilistic programming Logic programming Delimited continuations Algebraic effect handlers Machine learning

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probabilistic programming for Theory of Mind for Autonomous Decision Making

Probabilistic Programming for Theory of Mind for Autonomous ...

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作者： Seaman, Iris Rubi Brigham Young University

学位级别：M.Sc., Master of Science

As autonomous agents (such as unmanned aerial vehicles, or UAVs) become more ubiquitous, they are being used for increasingly complex tasks. Eventually, they will have to reason about the mental state of other agents, including those agents' beliefs, desires and goals—so-called Theory of Mind—and make decisions based on that reasoning. We describe increasingly complex theory of mind models of a UAV pursuing an intruder, and show that (1) there is a natural Bayesian formulation to reasoning about the uncertainty inherent in our estimate of another agent's mental state, and that (2) probabilistic programming is a natural way to describe models that involve one agent reasoning about another agent, where the target agent uses complex primitives such as path planners and saliency maps to make decisions. We propose a nested self-normalized importance sampling inference algorithm for probabilistic programs, and show that it can be used with planning-as-inference to simultaneously reason about other agents' plans and craft counter plans. We demonstrate that more complex models lead to improved performance, and that nested modeling manifests a wide variety of rational agent behavior.

关键词： probabilistic programming Autonomous decision making Theory of Mind

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