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检索条件"主题词=Probabilistic Programming"

共 320 条记录，以下是61-70 订阅

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Statically and Dynamically Delayed Sampling for Typed probabilistic programming Languages 17

Statically and Dynamically Delayed Sampling for Typed Probab...

引用

17th ACM SIGPLAN International Conference on Software Language Engineering (SLE)

作者： Caylak, Gizem Lunden, Daniel Senderov, Viktor Broman, David KTH Royal Inst Technol Stockholm Sweden Oracle Stockholm Sweden Ecole Normale Super Paris France

ISBN: (纸本)9798400711800

probabilistic programming languages (PPLs) make it possible to separate the concerns between probabilistic models and Bayesian inference algorithms. However, to make such inference efficient is technically very challenging, both in terms of execution time performance and inference accuracy. One successful optimization approach is the previously published work on dynamically delayed sampling. This runtime method makes use of analytical relations between random variables to reduce inference variance;however, tracking these relations introduces runtime overhead. Furthermore, implementing the dynamic approach in a statically typed language introduces type problems because delaying the sampling of random variables changes their types. Our work advances the state-of-the-art in two aspects. Firstly, to reduce the runtime overhead, we develop a compile-time version of delayed sampling. By incorporating optimization procedures during compilation, we eliminate the need for runtime relation tracking and consequent overhead. However, the compile-time version may not always be effective due to the program's possible dynamic behavior, such as stochastic branches, or the complexity of handling recursion. Secondly, we introduce constructs to implement dynamically delayed sampling in a statically typed universal PPL. Dynamically delayed sampling in statically typed languages is a viable optimization for complex Bayesian models, whereas simple models ought to be statically optimized. We evaluate both statically and dynamically delayed sampling on real-world examples, such as latent Dirichlet allocation and epidemiology models, and implement the methods in a statically typed PPL, Miking CorePPL.

关键词： probabilistic programming Bayesian Inference Delayed Sampling Automation

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Bean Machine: A Declarative probabilistic programming Language For Efficient Programmable Inference 10

Bean Machine: A Declarative Probabilistic Programming Langua...

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10th International Conference on probabilistic Graphical Models (PGM)

作者： Tehrani, Nazanin Arora, Nimar S. Li, Yucen Lily Shah, Kinjal Divesh Noursi, David Tingley, Michael Torabi, Narjes Masouleh, Sepehr Lippert, Eric Meijer, Erik Facebook Inc Menlo Pk CA 94025 USA

A number of imperative probabilistic programming Languages (PPLs) have been recently proposed, but the imperative style choice makes it very hard to deduce the dependence structure between the latent variables, which can also change from iteration to iteration. We propose a new declarative style PPL, Bean Machine, and demonstrate that in this new language, the dynamic dependence structure is readily available. Although we are not the first to propose a declarative PPL or to observe the advantages of knowing the dependence structure, we take the idea further by showing other inference techniques that become feasible or easier in this style. We show that it is very easy for users to program inference by composition (combining different inference techniques for different parts of the model), customization (providing a custom hand-written inference method for specific variables), and blocking (specifying blocks of random variables that should be sampled together) in a declarative language. A number of empirical results are provided where we backup these claims modulo the runtime inefficiencies of unvectorized Python. As a fringe benefit, we note that it is very easy to translate statistical models written in mathematical notation into our language.

关键词： probabilistic programming Programmable Inference Declarative Structure

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Modeling genome evolution with a DSEL for probabilistic programming

Modeling genome evolution with a DSEL for probabilistic prog...

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8th International Symposium on Practical Aspects of Declarative Languages

作者： Erwig, M Kollmansberger, S Oregon State Univ Sch EECS Corvallis OR 97331 USA

ISBN: (纸本)3540309470

Many scientific applications benefit from simulation. However, programming languages used in simulation, such as C++ or Matlab, approach problems from a deterministic procedural view, which seems to differ, in general, from many scientists' mental representation. We apply a domain-specific language for probabilistic programming to the biological field of gene modeling, showing how the mental-model gap may be bridged. Our system assisted biologists in developing a model for genome evolution by separating the concerns of model and simulation and providing implicit probabilistic non-determinism.

关键词： functional programming probabilistic programming haskell genome evolution

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How Attacker Knowledge Affects Privacy Risks: An Analysis Using probabilistic programming 8

How Attacker Knowledge Affects Privacy Risks: An Analysis Us...

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ACM International Workshop on Security and Privacy Analytics (IWSPA) / 12th ACM Annual Conference on Data and Applications Security and Privacy (CODASPY)

作者： Halvorsen, Louise Steffensen, Siv L. Rafnsson, Willard Kulyk, Oksana Pardo, Raul IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9781450392303

Governments and businesses routinely disclose large amounts of private data on individuals, for data analytics. However, despite attempts by data controllers to anonymise data, attackers frequently deanonymise disclosed data by matching it with their prior knowledge. When is a chosen anonymisation method adequate? For this, a data controller must consider attackers befitting their scenario;how does attacker knowledge affect disclosure risk? We present a multi-dimensional conceptual framework for assessing privacy risks given prior knowledge about data. The framework defines three dimensions: distinctness (of input records), informedness (of attacker), and granularity (of anonymisation program output). We model three well-known types of disclosure risk: identity disclosure, attribute disclosure, and quantitative attribute disclosure. We demonstrate how to apply this framework in a health record privacy scenario: We analyse how informing the attacker with COVID-19 infection rates affects privacy risks. We perform this analysis using Privug, a method that uses probabilistic programming to do standard statistical analysis with Bayesian Inference.

关键词： privacy anonymization probabilistic programming health privacy

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SPPL: probabilistic programming with Fast Exact Symbolic Inference 2021

SPPL: Probabilistic Programming with Fast Exact Symbolic Inf...

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42nd ACM SIGPLAN International Conference on programming Language Design and Implementation (PLDI)

作者： Saad, Feras A. Rinard, Martin C. Mansinghka, Vikash K. MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781450383912

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 probabilistic programs into sum-product expressions, a new symbolic representation and associated semantic domain that extends standard sum-product networks to support mixed-type distributions, numeric transformations, logical formulas, and pointwise and set-valued constraints. We formalize SPPL via a novel translation strategy from probabilistic programs to sum-product expressions and give sound exact algorithms for conditioning on and computing probabilities of events. SPPL imposes a collection of restrictions on probabilistic programs to ensure they can be translated into sum-product expressions, which allow the system to leverage new techniques for improving the scalability of translation and inference by automatically exploiting probabilistic structure. We implement a prototype of SPPL with a modular architecture and evaluate it on benchmarks the system targets, showing that it obtains up to 3500x speedups over state-of-the-art symbolic systems on tasks such as verifying the fairness of decision tree classifiers, smoothing hidden Markov models, conditioning transformed random variables, and computing rare event probabilities.

关键词： probabilistic programming symbolic execution

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Let's Chance: Playful probabilistic programming for Children

Let's Chance: Playful Probabilistic Programming for Children

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ACM CHI Conference on Human Factors in Computing Systems (CHI)

作者： Dharlwal, Manuj Dhariwal, Shruti MIT Media Lab Cambridge MA 02139 USA

ISBN: (纸本)9781450368193

probabilistic thinking has been one of the most powerful ideas in the history of science, and it is rapidly gaining even more relevance as it lies at the core of artificial intelligence (AI) systems and machine learning (ML) algorithms that are increasingly pervading our everyday lives. In this paper, we introduce Let's Chance-a novel computational microworld that extends the widely popular Scratch programming Language with new types of code blocks and representations that make it accessible for children to encounter and tinker with the rich ideas and sophisticated concepts of probabilistic modeling and learning. Using the tool, children can imagine and code their own expressive, playful, and personally meaningful probabilistic projects, such as-generative art, music, or text;chance-based games and stories;interactive visualizations;and even advanced projects for making a computer learn from input data using simple Markov models of probabilistic learning, among many other creative possibilities.

关键词： probabilistic programming creative coding AI education Scratch probability children learning

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Estimation of Energy Production by Using probabilistic programming Methods in Solar Power Plants: The case of Gazi Technopark 8

Estimation of Energy Production by Using Probabilistic Progr...

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International Conference on Renewable Energy Research and Applications

作者： Demirtas, Mehmet Akkoyun, Nuran Akkoyun, Emrah Cetinbas, Ipek Gazi Univ Dept Elect & Elect Engn Ankara Turkey Gazi Univ Grad Sch Nat & Appl Sci Ankara Turkey Middle East Tech Univ Dept Med Informat Ankara Turkey Eskisehir Osmangazi Univ Dept Elect & Elect Engn Eskisehir Turkey

ISBN: (纸本)9781728135878

In this study, energy production estimation was carried out by using probabilistic programming methods for Gazi Technopark Solar Power Plant (SPP). The measurements obtained from meteorological stations and energy analyzers established within the SPP were created by the real-time recorded data for a year. After analyzing the effect of the measurements on production and selecting the statistically significant ones, energy production was estimated. Firstly, maximum a posteriori (MAP) method was used and the energy production was estimated with 86% successful. Secondly, using a Bayesian approach, which fundamentally enhances subjective belief by the probability of the event, via incorporation of meteorological measurements, was updated the prediction of energy production. In this way, the success of estimation associated with a confidence interval was demonstrated using a probabilistic programming. The probabilistic estimation model, which was specified for the measurements obtained from Gazi Technopark SPP, can be applied for the estimation of energy production in other power plants as well and useful for planning of energy production.

关键词： solar energy energy production estimation machine learning probabilistic programming

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Deployable probabilistic programming

Deployable Probabilistic Programming

引用

ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on programming and Software (Onward) part of SPLASH Conference

作者： Tolpin, David PUB Tel Aviv Israel

ISBN: (纸本)9781450369954

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 facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern general-purpose programming languages. probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo's performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks.

关键词： probabilistic programming algorithmic differentiation bayesian modeling

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Suspension Analysis and Selective Continuation-Passing Style for Universal probabilistic programming Languages 33rd

Suspension Analysis and Selective Continuation-Passing Style...

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33rd European Symposium on programming (ESOP)

作者： Lunden, Daniel Hummelgren, Lars Kudlicka, Jan Eriksson, Oscar Broman, David Oracle Stockholm Sweden KTH Royal Inst Technol EECS & Digital Futures Stockholm Sweden BI Norwegian Business Sch Dept Data Sci & Analyt Oslo Norway Stanford Univ Comp Sci Dept Stanford CA 94305 USA

ISBN: (纸本)9783031572661;9783031572678

Universal probabilistic programming languages (PPLs) make it relatively easy to encode and automatically solve statistical inference problems. To solve inference problems, PPL implementations often apply Monte Carlo inference algorithms that rely on execution suspension. State-of-the-art solutions enable execution suspension either through (i) continuation-passing style (CPS) transformations or (ii) efficient, but comparatively complex, low-level solutions that are often not available in high-level languages. CPS transformations introduce overhead due to unnecessary closure allocations-a problem the PPL community has generally overlooked. To reduce overhead, we develop a new efficient selective CPS approach for PPLs. Specifically, we design a novel static suspension analysis technique that determines parts of programs that require suspension, given a particular inference algorithm. The analysis allows selectively CPS transforming the program only where necessary. We formally prove the correctness of the analysis and implement the analysis and transformation in the Miking CorePPL compiler. We evaluate the implementation for a large number of Monte Carlo inference algorithms on real-world models from phylogenetics, epidemiology, and topic modeling. The evaluation results demonstrate significant improvements across all models and inference algorithms.

关键词： probabilistic programming Static analysis Continuation-passing style

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Towards Compositional probabilistic programming

Towards Compositional Probabilistic Programming

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作者： Sennesh, Eli Northeastern University

学位级别：Ph.D., Doctor of Philosophy

Converging evidence from the behavioral sciences suggests that inference in probabilistic generative models can capture how human and animal subjects solve a variety of cognitive tasks involving uncertainty. In fact, such evidence has accumulated more quickly than bespoke computational modeling can keep pace, contributing impetus to the development of probabilistic programming systems to reduce the duplication of effort. Among these, deep probabilistic programming systems enable the integration of deep neural networks into probabilistic generative models as powerful function approximators. In this dissertation, I provide compositional tools for reasoning about model structure and inference in the deep probabilistic programming system probabilistic Torch. I begin with a case report about applying deep probabilistic programming to neuroimaging, including mean-field inference and a nontrivial stochastic gradient estimator for the training objective. I then dive into my work on training neural proposals to break down a complex joint distribution into a series of simpler complete conditional distributions. I then describe my contributions to a domain-specific language of inference combinators for composing importance sampling strategies in deep probabilistic programs, including an inference combinators implementation of the previous decompositional sampling strategy. I finally overview my work on providing categorical foundations in which to ground the semantics of deep probabilistic programs and reason compositionally about their model structure and inference strategies. I then close the dissertation by discussing how compositionality can help probabilistic machine learning scale up towards brain-sized inference problems.

关键词： probabilistic programming Computational modeling Machine learning Neuroimaging

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