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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： S. V. Kozlov E. A. Voronin Federal State Institution «Federal Research Center «Informatics and Management» of the Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781728147727

ISBN: (纸本)9781728147734

A mathematical approach is presented using a unified indicator of assessing economic security in the field of creating on-board complexes of technical equipment as a payload for installation on a given technical platform, based on an assessment of the probability of various kinds of threats and the probability of their successful reflection. Various methods for estimating probabilities are presented, their capabilities and applications are revealed. It is shown that machine learning methods in combination with probabilistic programming are most suitable for the requirements of the digital economy. The analysis of the possibilities of using machine learning technologies and assessing the probability of programming in assessing and predicting economic security in the formation of onboard automation systems based on the rear platform is carried out.

关键词： complexation of onboard equipment on the platform complex process economic security analysis and modeling of random processes Markov chains Bayesian networks probabilistic programming Bayesian analysis Bayesian networks Markov chain Probability Bayes methods automation system Artificial Intelligence Machine Learning Tree graphs Platform Learning methods programming

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programming Uncertain hings 16

Programming Uncertain <T>hings

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Proceedings of the Twenty-First International Conference on Architectural Support for programming Languages and Operating Systems

作者： Kathryn S. McKinley Microsoft Research Redmond WA USA

ISBN: (纸本)9781450340915

Innovation flourishes with good abstractions. For instance, codification of the IEEE Floating Point standard in 1985 was critical to the subsequent success of scientific computing. programming languages currently lack appropriate abstractions for uncertain data. Applications already use estimates from sensors, machine learning, big data, humans, and approximate algorithms, but most programming languages do not help developers address correctness, programmability, and optimization problems due to *** address these problems, we propose a new programming abstraction called Uncertain embedded into languages, such as C#, C++, Java, Python, and JavaScript. Applications use familiar discrete operations for estimates with Uncertain. Overloaded conditional operators specify hypothesis tests and applications use them to control false positives and negatives. A simple compositional operator expresses domain knowledge. We carefully restrict expressiveness such that we can build a runtime that implements correct statistical reasoning at conditionals. Our system relieves developers of the need to implement or deeply understand statistics. We demonstrate substantial programmability, correctness, and efficiency benefits of this programming model for GPS sensor navigation, approximate computing, machine learning, and *** encourage the community to develop and use abstractions for estimates.

关键词： probabilistic programming bayesian logic lazy evaluation languages programming with estimates measurement verification

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Static Analysis for probabilistic Programs: Inferring Whole Program Properties from Finitely Many Paths 13

Static Analysis for Probabilistic Programs: Inferring Whole ...

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ACM SIGPLAN Conference on programming Language Design and Implementation

作者： Sriram Sankaranarayanan Aleksandar Chakarov Sumit Gulwani University of Colorado Microsoft Research

ISBN: (纸本)9781450320146

We propose an approach for the static analysis of probabilistic programs that sense, manipulate, and control based on uncertain data. Examples include programs used in risk analysis, medical decision making and cyber-physical systems. Correctness properties of such programs take the form of queries that seek the probabilities of assertions over program variables. We present a static analysis approach that provides guaranteed interval bounds on the values (assertion probabilities) of such queries. First, we observe that for probabilistic programs, it is possible to conclude facts about the behavior of the entire program by choosing a finite, adequate set of its paths. We provide strategies for choosing such a set of paths and verifying its adequacy. The queries are evaluated over each path by a combination of symbolic execution and probabilistic volume-bound computations. Each path yields interval bounds that can be summed up with a "coverage" bound to yield an interval that encloses the probability of assertion for the program as a whole. We demonstrate promising results on a suite of benchmarks from many different sources including robotic manipulators and medical decision making programs.

关键词： probabilistic programming Program Verification Volume Bounding Symbolic Execution Monte-Carlo Sampling

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Mastering uncertainty in performance estimations of configurable software systems 20

Mastering uncertainty in performance estimations of configur...

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Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

作者： Johannes Dorn Sven Apel Norbert Siegmund Leipzig University Saarland University

ISBN: (纸本)9781450367684

Understanding the influence of configuration options on performance is key for finding optimal system configurations, system understanding, and performance debugging. In prior research, a number of performance-influence modeling approaches have been proposed, which model a configuration option's influence and a configuration's performance as a scalar value. However, these point estimates falsely imply a certainty regarding an option's influence that neglects several sources of uncertainty within the assessment process, such as (1) measurement bias, (2) model representation and learning process, and (3) incomplete data. This leads to the situation that different approaches and even different learning runs assign different scalar performance values to options and interactions among them. The true influence is uncertain, though. There is no way to quantify this uncertainty with state-of-the-art performance modeling approaches. We propose a novel approach, P4, based on probabilistic programming that explicitly models uncertainty for option influences and consequently provides a confidence interval for each prediction of a configuration's performance alongside a scalar. This way, we can explain, for the first time, why predictions may cause errors and which option's influences may be unreliable. An evaluation on 12 real-world subject systems shows that P4's accuracy is in line with the state of the art while providing reliable confidence intervals, in addition to scalar predictions.

关键词： probabilistic programming performance-influence modeling configurable software systems P4

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Designing Perceptual Puzzles by Differentiating probabilistic Programs 22

Designing Perceptual Puzzles by Differentiating Probabilisti...

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ACM SIGGRAPH 2022 Conference Proceedings

作者： Kartik Chandra Tzu-Mao Li Joshua Tenenbaum Jonathan Ragan-Kelley Computer Science & Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology (MIT) United States of America Department of Computer Science and Engineering (CSE) University of California San Diego United States of America Department of Brain and Cognitive Sciences (BCS) Center for Brains Minds & Machines (CBMM) Computer Science & Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology (MIT) United States of America

ISBN: (纸本)9781450393379

We design new visual illusions by finding “adversarial examples” for principled models of human perception — specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we design a differentiable probabilistic programming language, whose API exposes MCMC inference as a first-class differentiable function. We demonstrate our method by automatically creating illusions for three features of human vision: color constancy, size constancy, and face perception.

关键词： differentiable programming illusions probabilistic programming

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Uncertain: a first-order type for uncertain data 14

Uncertain<T>: a first-order type for uncertain data

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Proceedings of the 19th international conference on Architectural support for programming languages and operating systems

作者： James Bornholt Todd Mytkowicz Kathryn S. McKinley Australian National University Canberra Australia Microsoft Research Redmond WA USA

ISBN: (纸本)9781450323055

Emerging applications increasingly use estimates such as sensor data (GPS), probabilistic models, machine learning, big data, and human data. Unfortunately, representing this uncertain data with discrete types (floats, integers, and booleans) encourages developers to pretend it is not probabilistic, which causes three types of uncertainty bugs. (1) Using estimates as facts ignores random error in estimates. (2) Computation compounds that error. (3) Boolean questions on probabilistic data induce false positives and negatives. This paper introduces Uncertain, a new programming language abstraction for uncertain data. We implement a Bayesian network semantics for computation and conditionals that improves program correctness. The runtime uses sampling and hypothesis tests to evaluate computation and conditionals lazily and efficiently. We illustrate with sensor and machine learning applications that Uncertain improves expressiveness and *** previous probabilistic programming languages focus on experts, Uncertain serves a wide range of developers. Experts still identify error distributions. However, both experts and application writers compute with distributions, improve estimates with domain knowledge, and ask questions with conditionals. The Uncertain type system and operators encourage developers to expose and reason about uncertainty explicitly, controlling false positives and false negatives. These benefits make Uncertain a compelling programming model for modern applications facing the challenge of uncertainty.

关键词： uncertain data estimates probabilistic programming

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AcMC 2 : Accelerating Markov Chain Monte Carlo Algorithms for probabilistic Models 19

AcMC 2 : Accelerating Markov Chain Monte Carlo Algorithms fo...

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Proceedings of the Twenty-Fourth International Conference on Architectural Support for programming Languages and Operating Systems

作者： Subho S. Banerjee Zbigniew T. Kalbarczyk Ravishankar K. Iyer University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (纸本)9781450362405

probabilistic models (PMs) are ubiquitously used across a variety of machine learning applications. They have been shown to successfully integrate structural prior information about data and effectively quantify uncertainty to enable the development of more powerful, interpretable, and efficient learning algorithms. This paper presents AcMC2, a compiler that transforms PMs into optimized hardware accelerators (for use in FPGAs or ASICs) that utilize Markov chain Monte Carlo methods to infer and query a distribution of posterior samples from the model. The compiler analyzes statistical dependencies in the PM to drive several optimizations to maximally exploit the parallelism and data locality available in the problem. We demonstrate the use of AcMC2 to implement several learning and inference tasks on a Xilinx Virtex-7 FPGA. AcMC2-generated accelerators provide a 47-100× improvement in runtime performance over a 6-core IBM Power8 CPU and a 8-18× improvement over an NVIDIA K80 GPU. This corresponds to a 753-1600× improvement over the CPU and 248-463× over the GPU in performance-per-watt terms.

关键词： probabilistic programming accelerator probabilistic graphical models markov chain monte carlo

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WATER QUALITY MANAGEMENT in the HSINTIEN RIVER IN TAIWAN

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JAWRA Journal of the American Water Resources Association 1978年第3期14卷 689-695页

作者： Lohani, B.N. Thanh, N.C. Environmental Engineering Division Asian Institute of Technology Bangkok P. O. Box 2754 Thailand

ABSTRACT: The objective of cost effectiveness has led to the use of mathematical decision models to implement the best water quality control program in a river from the various alternatives available at a time. The paper presents the water quality control program in the Hsintien River in Taiwan by the use of probabilistic programming technique. Copyright © 1978, Wiley Blackwell. All rights reserved

关键词： cost effectiveness decision model probabilistic programming water quality management

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Bayesian hierarchical spatial models: Implementing the Besag York Mollie model in stan

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SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY 2019年 31卷 100301-000页

作者： Morris, Mitzi Wheeler-Martin, Katherine Simpson, Dan Mooney, Stephen J. Gelman, Andrew DiMaggio, Charles Columbia Univ Inst Social & Econ Res & Policy New York NY USA NYU Dept Surg Sch Med New York NY 10016 USA Univ Toronto Dept Stat Sci Toronto ON Canada Univ Washington Dept Epidemiol Seattle WA 98195 USA Columbia Univ Dept Stat New York NY USA

This report presents a new implementation of the Besag-York-Mollie (BYM) model in Stan, a probabilistic programming platform which does full Bayesian inference using Hamiltonian Monte Carlo (HMC). We review the spatial auto-correlation models used for areal data and disease risk mapping, and describe the corresponding Stan implementations. We also present a case study using Stan to fit a BYM model for motor vehicle crashes injuring schoolage pedestrians in New York City from 2005 to 2014 localized to census tracts. Stan efficiently fit our multivariable BYM model having a large number of observations (n=2095 census tracts) with small outcome counts < 10 in the majority of tracts. Our findings reinforced that neighborhood income and social fragmentation are significant correlates of school-age pedestrian injuries. We also observed that nationally-available census tract estimates of commuting methods may serve as a useful indicator of underlying pedestrian densities. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Bayesian inference Intrinsic conditional auto-regressive model Besag-York-Mollie model probabilistic programming Stan Pedestrian injuries

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BayesPy: variational Bayesian inference in Python

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Jaakko Luttinen Google MPI for Intelligent Systems Department of Computer Science Aalto University Finland

BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model construction and efficient inference make BayesPy suitable for both average and expert Bayesian users. It also supports some advanced methods such as stochastic and collapsed variational inference.

关键词： Python probabilistic programming variational Bayes

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