检索结果-内蒙古大学图书馆

programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2017年第2期26卷 403-413页

作者： de Valpine, Perry Turek, Daniel Paciorek, Christopher J. Anderson-Bergman, Clifford Lang, Duncan Temple Bodik, Rastislav Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Univ Calif Davis Dept Stat Davis CA 95616 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification, NIMBLE extends the BUGS language and creates model objects, which can manipulate variables, calculate log probability values, generate simulations, and query the relationships among variables. For algorithm programming, NIMBLE provides functions that operate with model objects using two stages of evaluation. The first stage allows specialization of a function to a particular model and/or nodes, such as creating a Metropolis-Hastings sampler for a particular block of nodes. The second stage allows repeated execution of computations using the results of the first stage. To achieve efficient second-stage computation, NIMBLE compiles models and functions via C++, using the Eigen library for linear algebra, and provides the user with an interface to compiled objects. The NIMBLE language represents a compilable domain-specific language (DSL) embedded within R. This article provides an overview of the design and rationale for NIMBLE along with illustrative examples including importance sampling, Markov chain Monte Carlo (MCMC) and Monte Carlo expectation maximization (MCEM). Supplementary materials for this article are available online.

关键词： Domain-specific language Hierarchical models MCEM MCMC probabilistic programming R

来源：评论

学校读者我要写书评

暂无评论

Semantics for Variational Quantum programming

引用

PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2022年第POPL期6卷 1–31页

作者： Jia, Xiaodong Kornell, Andre Lindenhovius, Bert Mislove, Michael Zamdzhiev, Vladimir Hunan Univ Sch Math Changsha 410082 Peoples R China Tulane Univ Dept Comp Sci New Orleans LA 70118 USA Johannes Kepler Univ Linz Inst Math Methods Med & Database Modelling Linz Austria Univ Lorraine INRIA LORIA F-54000 Nancy France

We consider a programming language that can manipulate both classical and quantum information. Our language is type-safe and designed for variational quantum programming, which is a hybrid classical-quantum computational paradigm. The classical subsystem of the language is the probabilistic FixPoint Calculus (PFPC), which is a lambda calculus with mixed-variance recursive types, term recursion and probabilistic choice. The quantum subsystem is a first-order linear type system that can manipulate quantum information. The two subsystems are related by mixed classical/quantum terms that specify how classical probabilistic effects are induced by quantum measurements, and conversely, how classical (probabilistic) programs can influence the quantum dynamics. We also describe a sound and computationally adequate denotational semantics for the language. Classical probabilistic effects are interpreted using a recently-described commutative probabilistic monad on DCPO. Quantum effects and resources are interpreted in a category of von Neumann algebras that we show is enriched over (continuous) domains. This strong sense of enrichment allows us to develop novel semantic methods that we use to interpret the relationship between the quantum and classical probabilistic effects. By doing so we provide a very detailed denotational analysis that relates domain-theoretic models of classical probabilistic programming to models of quantum programming.

关键词： Variational Quantum programming probabilistic programming Semantics

来源：评论

学校读者我要写书评

暂无评论

Use of technological constraints as enumeration criteria for the solution of a travelling salesman problem applicable to production machinery

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2000年第1期214卷 61-77页

作者： Khan, WA Khan, MA Hayhurst, DR GIK Inst Engn Sci & Technol Fac Mech Engn Topi 23460 District Swabi Pakistan GIK Inst Engn Sci & Technol Fac Comp Sci & Engn Topi Pakistan Univ Manchester Inst Sci & Technol Dept Mech Engn Manchester M60 1QD Lancs England

The routing of tools, dies, torches, electric area, ray beams and jets to produce features on a workpiece is a travelling salesman problem (TSP) of variable size and unpredictable node distribution. The orientation of the nodes may be in two-dimensional planes or three-dimensional spaces. An optimal path for the TSP can be found using tour construction, subtour elimination and tour-to-tour improvement procedures. Exact solution of the TSP for large numbers of nodes incurs a high computing cost. Enumeration of the problem domain can lead to a globally optimal solution at reduced computing cost. Technological constraints can be used to reduce the problem size for formulation as a TSP under multiple constraints. The solution of a TSP with constraints results in large savings in non-productive movement with no changes in productive movement. The savings in non-productive movement in repetitive batch-type manufacturing lead to reduced machine tool residence time and lower power consumption.

关键词： combinatorial analysis probabilistic programming production routing travelling salesman

来源：评论

学校读者我要写书评

暂无评论

Rule-based Bayesian regression

引用

STATISTICS AND COMPUTING 2022年第3期32卷 1-17页

作者： Botsas, Themistoklis Mason, Lachlan R. Pan, Indranil Alan Turing Inst London England Imperial Coll London London England

We introduce a novel rule-based approach for handling regression problems. The new methodology carries elements from two frameworks: (i) it provides information about the uncertainty of the parameters of interest using Bayesian inference, and (ii) it allows the incorporation of expert knowledge through rule-based systems. The blending of those two different frameworks can be particularly beneficial for various domains (e.g., engineering), where even though the significance of uncertainty quantification motivates a Bayesian approach, there is no simple way to incorporate researcher intuition into the model. We validate our models by applying them to synthetic applications: a simple linear regression problem and two more complex structures based on partial differential equations, and we illustrate their use through two cases derived from real data. Finally, we review the advantages of our methodology, which include the simplicity of the implementation, the uncertainty reduction due to the added information and, in some occasions, the derivation of better point predictions, and we outline limitations, mainly from the computational complexity perspective, such as the difficulty in choosing an appropriate algorithm and the added computational burden.

关键词： probabilistic programming Bayesian Inference Advection-diffusion B-splines Gaussian processes

来源：评论

学校读者我要写书评

暂无评论

Modular probabilistic Models via Algebraic Effects

引用

PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2022年第ICFP期6卷 381–410页

作者： Nguyen, Minh Perera, Roly Wang, Meng Wu, Nicolas Univ Bristol Bristol Avon England Alan Turing Inst London England Imperial Coll London London England

probabilistic programming languages (PPLs) allow programmers to construct statistical models and then simulate data or perform inference over them. Many PPLs restrict models to a particular instance of simulation or inference, limiting their reusability. In other PPLs, models are not readily composable. Using Haskell as the host language, we present an embedded domain specific language based on algebraic effects, where probabilistic models are modular, first-class, and reusable for both simulation and inference. We also demonstrate how simulation and inference can be expressed naturally as composable program transformations using algebraic effect handlers.

关键词： probabilistic programming effect handlers modularity embedded domain-specific languages functional programming

来源：评论

学校读者我要写书评

暂无评论

Contextual Equivalence for a probabilistic Language with Continuous Random Variables and Recursion

引用

PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2018年第ICFP期2卷 1–30页

作者： Wand, Mitchell Culpepper, Ryan Giannakopoulos, Theophilos Cobb, Andrew Northeastern Univ Coll Comp & Informat Sci 360 Huntington AveRoom 202WVH Boston MA 02115 USA Czech Tech Univ Fac Informat Technol Thakurova 9 Prague 16000 Czech Republic BAE Syst FAST Labs 600 Dist Ave Burlington MA 01803 USA

We present a complete reasoning principle for contextual equivalence in an untyped probabilistic language. The language includes continuous (real-valued) random variables, conditionals, and scoring. It also includes recursion, since the standard call-by-value fixpoint combinator is expressible. We demonstrate the usability of our characterization by proving several equivalence schemas, including familiar facts from lambda calculus as well as results specific to probabilistic programming. In particular, we use it to prove that reordering the random draws in a probabilistic program preserves contextual equivalence. This allows us to show, for example, that (let x = e(1) in let y = e(2) in e(0)) =(ctx) (let y = e(2) in let x = e(1) in e(0)) (provided x does not occur free in e(2) and y does not occur free in e(1)) despite the fact that e(1) and e(2) may have sampling and scoring effects.

关键词： probabilistic programming logical relations contextual equivalence

来源：评论

学校读者我要写书评

暂无评论

Past, Present and Future of Software for Bayesian Inference

引用

STATISTICAL SCIENCE 2024年第1期39卷 46-61页

作者： Strumbelj, Erik Bouchard-Cote, Alexandre Corander, Jukka Gelman, Andrew Rue, Havard Murray, Lawrence Pesonen, Henri Plummer, Martyn Vehtari, Aki Univ Ljubljana Fac Comp & Informat Sci Ljubljana Slovenia Univ British Columbia Stat Vancouver BC Canada Univ Oslo Fac Med Oslo Norway Univ Helsinki Fac Sci Helsinki Finland Wellcome Sanger Inst Cambridge England Columbia Univ Stat & Polit Sci New York NY 10027 USA King Abdullah Univ Sci & Technol Stat Thuwal Saudi Arabia Oslo Univ Hosp Oslo Ctr Biostat & Epidemiol Oslo Norway Univ Warwick Stat Warwick England Aalto Univ Computat Bayesian Modeling Aalto Finland

Software tools for Bayesian inference have undergone rapid evolution in the past three decades, following popularisation of the first generation MCMC-sampler implementations. More recently, exponential growth in the number of users has been stimulated both by the active development of new packages by the machine learning community and popularity of specialist software for particular applications. This review aims to summarize the most popular software and provide a useful map for a reader to navigate the world of Bayesian computation. We anticipate a vigorous continued development of algorithms and corresponding software in multiple research fields, such as probabilistic programming, likelihood -free inference and Bayesian neural networks, which will further broaden the possibilities for employing the Bayesian paradigm in exciting applications.

关键词： Statistics data analysis MCMC computation probabilistic programming

来源：评论

学校读者我要写书评

暂无评论

Scaling Exact Inference for Discrete probabilistic Programs

引用

PROCEEDINGS OF THE ACM ON programming LANGUAGES-PACMPL 2020年第OOPSLA期4卷 1–31页

作者： Holtzen, Steven Van den Broeck, Guy Millstein, Todd Univ Calif Los Angeles Los Angeles CA 90095 USA

probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of probabilistic inference remains the primary roadblock for applying PPLs in practice. Inference is fundamentally hard, so there is no one-size-fits all solution. In this work, we target scalable inference for an important class of probabilistic programs: those whose probability distributions are discrete. Discrete distributions are common in many fields, including text analysis, network verification, artificial intelligence, and graph analysis, but they prove to be challenging for existing PPLs. We develop a domain-specific probabilistic programming language called Dice that features a new approach to exact discrete probabilistic program inference. Dice exploits program structure in order to factorize inference, enabling us to perform exact inference on probabilistic programs with hundreds of thousands of random variables. Our key technical contribution is a new reduction from discrete probabilistic programs to weighted model counting (WMC). This reduction separates the structure of the distribution from its parameters, enabling logical reasoning tools to exploit that structure for probabilistic inference. We (1) show how to compositionally reduce Dice inference to WMC, (2) prove this compilation correct with respect to a denotational semantics, (3) empirically demonstrate the performance benefits over prior approaches, and (4) analyze the types of structure that allow Dice to scale to large probabilistic programs.

关键词： probabilistic programming

来源：评论

学校读者我要写书评

暂无评论

ELVES Measurements in the "UV Atmosphere" (Mini-EUSO) Experiment Onboard the ISS and Their Reconstruction

引用

COSMIC RESEARCH 2024年第4期62卷 330-338页

作者： Sharakin, S. Barghini, D. Battisti, M. Belov, A. Bertaina, M. Bianciotto, M. Bisconti, F. Blaksley, C. Blin, S. Cambie, G. Capel, F. Casolino, M. Ebisuzaki, T. Eser, J. Fenu, F. Franceschi, M. A. Golzio, A. Gorodetsky, P. Kajino, F. Kasuga, H. Klimov, P. Manfrin, M. Marcelli, L. Marszal, W. Miyamoto, H. Mignone, M. Murashov, A. Napolitano, T. Ohmori, H. Olinto, A. Parizot, E. Picozza, P. Piotrowski, L. W. Plebaniak, Z. Prevot, G. Reali, E. Ricci, M. Romoli, G. Sakaki, N. Shinozaki, K. Szabelski, J. De la Taille, C. Takizawa, Y. Vrabel, M. Wiencke, L. Zotov, M. Moscow MV Lomonosov State Univ Skobeltsyn Inst Nucl Phys Moscow 119991 Russia INFN Sect Turin I-10125 Turin Italy Univ Turin Dept Phys I-10125 Turin Italy INAF Astrophys Observ Turin I-10025 Pino Torinese Italy Univ Paris Cite CNRS Astroparticule & Cosmol F-75013 Paris France Moscow MV Lomonosov State Univ Fac Phys Moscow 119991 Russia INFN Sect Rome Tor Vergata I-00133 Rome Italy RIKEN Wako 3510198 Japan Univ Roma Tor Vergata Dept Phys I-00133 Rome Italy Tech Univ Munich D-80333 Munich Germany Univ Chicago Dept Astron & Astrophys Chicago IL 60637 USA Agenzia Spaziale Italiana I-00133 Rome Italy INFN Natl Labs Frascati I-00044 Frascati Italy Konan Univ Dept Phys Kobe Hyogo 6588501 Japan Natl Ctr Nucl Res PL-05400 Otwock Poland Univ Warsaw Fac Phys Pasteura 5 PL-02093 Warsaw Poland Stefan Batory Acad Appl Sci PL-96100 Skierniewice Poland Ecole Polytech Omega CNRS IN2P3 Palaiseau I-91120 France Colorado Sch Mines Dept Phys Golden CO 80401 USA

More than three dozen submillisecond events of ELVES type ("elves"), which are the result of the interaction of the front of an electromagnetic pulse from a lightning discharge and the lower layer of the ionosphere, have been identified in the data of a UV Atmosphere orbital multichannel detector (Mini-EUSO). Each event has a characteristic annular glow pattern and occupies a significant part of the detector's field of view, and the signal in a separate channel has an asymmetric profile with a pronounced peak. The distribution of peak times contains information about both the localization of the discharge and the altitude of the glow. In this paper, we propose a Bayesian (probabilistic) model for reconstructing ELVES events, implemented using probabilistic programming methods in PyMC-5. The capabilities of the model for determining the position of the discharge are shown using the example of several events. Methods for modifying the model to restore the discharge orientation and refine the glow height are outlined.

关键词： UV atmosphere orbital detector transient atmospheric phenomena Bayesian inference probabilistic programming

来源：评论

学校读者我要写书评

暂无评论

Scalable Query Answering Under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach

引用

NEUROINFORMATICS 2023年第2期21卷 407-425页

作者： Zanitti, Gaston E. Soto, Yamil Iovene, Valentin Martinez, Maria Vanina Rodriguez, Ricardo O. Simari, Gerardo I. Wassermann, Demian INRIA Parietal Team 1 Rue Honore dEstienne dOrves F-91120 Palaiseau Ile De France France Univ Nacl Sur Dept Comp Sci & Engn San Andres 800 RA-8000 Bahia Blanca Buenos Aires Argentina Univ Nacl Sur CONICET Inst Comp Sci & Engn San Andres 800 RA-8000 Bahia Blanca Buenos Aires Argentina Univ Buenos Aires Dept Comp Sci Acceso Pabellon 1 2160 RA-1428 Buenos Aires DF Argentina Univ Buenos Aires Inst Comp Sci Res CONICET Acceso Pabellon 1 2160 RA-1428 Buenos Aires DF Argentina

Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, keywords associated with studies, etc. Furthermore, there is inherent uncertainty associated with brain scans arising from the mapping between voxels-3D pixels-and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In particular, one major weakness of current tools that attempt to address this task is that only very limited propositional query languages have been developed. In this paper we present NeuroLang, a probabilistic language based on first-order logic with existential rules, probabilistic uncertainty, ontologies integration under the open world assumption, and built-in mechanisms to guarantee tractable query answering over very large datasets. NeuroLang's primary objective is to provide a unified framework to seamlessly integrate heterogeneous data, such as ontologies, and map fine-grained cognitive domains to brain regions through a set of formal criteria, promoting shareable and highly reproducible research. After presenting the language and its general query answering architecture, we discuss real-world use cases showing how NeuroLang can be applied to practical scenarios.

关键词： Datalog Open-world Assumption probabilistic programming Query Answering Meta-Analysis Neuroimaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：