检索结果-内蒙古大学图书馆

A simulated annealing-based algorithm for selecting balanced samples

COMPUTATIONAL STATISTICS 2022年第1期37卷 491-505页

作者： Benedetti, Roberto Dickson, Maria Michela Espa, Giuseppe Pantalone, Francesco Piersimoni, Federica Univ G dAnnunzio Dept Econ Studies I-65127 Pescara Italy Univ Trento Dept Econ & Management I-38122 Trento Italy Univ Perugia Dept Econ I-06123 Perugia Italy Istat Directorate Methodol & Stat Proc Design I-00184 Rome Italy

Balanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

关键词： Balanced sampling Auxiliary variables sampling algorithms Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Boltzmann distribution on "short" integer partitions with power parts: Limit laws and sampling

引用

ADVANCES IN APPLIED MATHEMATICS 2024年 159卷

作者： Peyen, Jean C. Bogachev, Leonid, V Martin, Paul P. Univ Leeds Sch Math Leeds LS2 9JT England

The paper is concerned with the asymptotic analysis of a family of Boltzmann (multiplicative) distributions over the set Lambda(q) of strict integer partitions (i.e., with unequal parts) into perfect q-th powers. A combinatorial link is provided via a suitable conditioning by fixing the partition weight (the sum of parts) and length (the number of parts), leading to uniform distribution on the corresponding subspaces of partitions. The Boltzmann measure is calibrated through the hyper- parameters (N) and (M) controlling the expected weight and length, respectively. We study "short" partitions, where the parameter (M) is either fixed or grows slower than for typical partitions in Lambda(q). For this model, we obtain a variety of limit theorems including the asymptotics of the cumulative cardinality in the case of fixed (M) and a limit shape result in the case of slow growth of (M). In both cases, we also characterize the joint distribution of the weight and length, as well as the growth of the smallest and largest parts. Using these results we construct suitable sampling algorithms and analyze their performance. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons .org /licenses /by /4 .0/).

关键词： Integer partitions Boltzmann distribution Generating functions Young diagrams Limit shape sampling algorithms

来源：评论

学校读者我要写书评

暂无评论

Learning from mistakes: sampling strategies to efficiently train machine learning models for material property prediction

引用

COMPUTATIONAL MATERIALS SCIENCE 2023年 224卷

作者： Magar, Rishikesh Farimani, Amir Barati Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

Recent advances in machine learning (ML) based methodologies have accelerated the prediction of the physical properties of materials. These ML models, however, rely on large amounts of simulated or experimental data to make a reliable prediction. This dependence on large amounts of data can be a roadblock to building ML models since collecting the data is prohibitively expensive and time-consuming. In this work, we propose two sampling strategies to reliably train machine learning models in the lowest amounts of data. Our algorithms alleviate the need to generate large datasets to train machine learning models. We demonstrate the effectiveness of these sampling strategies by improving the performance of Crystal Graph Convolutional Neural Network (CGCNN) on four different datasets. Using the proposed strategies, we can reach the benchmark performance of CGCNN models in fewer data samples.

关键词： Machine learning sampling algorithms

来源：评论

学校读者我要写书评

暂无评论

sampling Exponentially Tilted Stable Distributions

引用

ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2011年第1期22卷 3-3页

作者： Hofert, Marius Swiss Fed Inst Technol Dept Math CH-8092 Zurich Switzerland

Several algorithms for sampling exponentially tilted positive stable distributions have recently been suggested. Three of them are known as exact methods, that is, neither do they rely on approximations nor on numerically critical procedures. One of these algorithms is outperformed by another one uniformly over all parameters. The remaining two algorithms are based on different ideas and both have their advantages. After a brief overview of sampling algorithms for exponentially tilted positive stable distributions, the two algorithms are compared. A rule is derived when to apply which for sampling these distributions.

关键词： Stable distributions exponentially tilted stable distributions Laplace-Stieltjes transforms sampling algorithms

来源：评论

学校读者我要写书评

暂无评论

Reliability increase of masonry characteristics estimation by a sampling algorithm applied to thermographic digital images

引用

PROBABILISTIC ENGINEERING MECHANICS 2020年 60卷 103022-103022页

作者： Cluni, Federico Costarelli, Danilo Gusella, Vittorio Vinti, Gianluca Univ Perugia Dept Civil & Environm Engn Via G Duranti 93 I-06125 Perugia Italy Univ Perugia Dept Math & Informat Via Vanvitelli 1 I-06123 Perugia Italy

In this paper the estimation of masonry characteristics by means of thermographic images, enhanced by sampling Kantorovich algorithm, is taken into account. In particular, the convergence of the Statistical Volume Element (SVE) to the Representative Volume Element (RVE) is analyzed. It is found that the enhancement, obtained by the proposed procedure, allows a faster convergence of SVE to RVE and a reduced coefficient of variation of the estimates obtained using a partition of the image with windows of smaller dimensions. Moreover, the effects of the uncertainties in the parameters involved in the reconstruction of texture from thermographic image, such as those used in morphological operators employed in digital image processing, involving the environmental conditions of the samples and the properties of the kernel utilized in the image enhancement algorithm, have been studied in order to assess their influence on the estimated mechanical characteristics. The performed analyses contribute to increase the reliability of the thermography as tool for identifying of masonries covered with plaster or frescoes, which is a very frequent case in vulnerability analysis of historical buildings.

关键词： Homogenization Masonry Thermography sampling algorithms Digital imaging

来源：评论

学校读者我要写书评

暂无评论

Efficient anomaly detection on sampled data streams with contaminated phase I data

引用

INTERNET TECHNOLOGY LETTERS 2020年第5期3卷

作者： El Sibai, Rayane Abdo, Jacques Bou Abou Jaoude, Chady Demerjian, Jacques Assaker, Joseph Makhoul, Abdallah Al Maaref Univ Fac Engn Beirut Lebanon Notre Dame Univ Fac Nat & Appl Sci Deir El Kamar Lebanon Antonine Univ Ticket Labaratory Fac Engn Baabda Lebanon Lebanese Univ LaRRIS Fac Sci Fanar Lebanon Univ Bourgogne Franche Comte UMR CNRS 6174 Femto ST Inst Besancon France

Control chart algorithms aim to monitor a process over time. This process consists of two phases. Phase I, also called the learning phase, estimates the normal process parameters, then in Phase II, anomalies are detected. However, the learning phase itself can contain contaminated data such as outliers. If left undetected, they can jeopardize the accuracy of the whole chart by affecting the computed parameters, which leads to faulty classifications and defective data analysis results. This problem becomes more severe when the analysis is done on a sample of the data rather than the whole data. To avoid such a situation, Phase I quality must be guaranteed. The purpose of this paper is to introduce a new approach for applying EWMA chart to obtain accurate anomaly detection results over sampled data even if contaminations exist in Phase I. The new chart is applied to a real dataset, and its performance is evaluated on both sampled and not sampled data according to several criteria.

关键词： anomalies detection data streams EWMA sampling algorithms

来源：评论

学校读者我要写书评

暂无评论

Recent advances in inferential solutions to inverse problems

引用

INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2008年第6期16卷 797-810页

作者： Fox, Colin Univ Otago Dept Phys Dunedin New Zealand

Inferential solutions to inverse problems provide substantial advantages over deterministic methods, such as: quantitative estimates with posterior (data-dependent) error estimates, predictive densities, model comparison and direct support for optimal decisions. The ability to include arbitrary forward maps, and hence use high-level representations of the unknowns, allows structure-preserving model reduction and also allows 'classification' to be performed within the 'imaging' step. Since inferential methods make (provably) optimal use of data, the ability to reduce data to a minimal set gives cost savings in applications where collecting data is expensive. The price of these advantages is presently the relatively high computational cost of sampling algorithms for computing estimates. Hence the most significant advances are in computational methods for sample-based inference in inverse problems. In this article we review the inferential formulation of inverse problems, some reasons why it is necessary to take on the extra machinery of inferential solutions, the 'basement level' methods for computing inferential solutions, and summarize some recent advances in computational methods for inferential solutions to inverse problems.

关键词： inverse problems Bayesian inference Metropolis Hastings sampling algorithms computational cost

来源：评论

学校读者我要写书评

暂无评论

Metropolis Monte Carlo sampling: convergence, localization transition and optimality

引用

JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT 2023年第12期2023卷 123205-123205页

作者： Chepelianskii, Alexei D. Majumdar, Satya N. Schawe, Hendrik Trizac, Emmanuel Univ Paris Saclay CNRS Lab Phys Solides F-91405 Orsay France Univ Paris Saclay CNRS LPTMS F-91405 Orsay France CY Cergy Paris Univ LPTM UMR 8089 CNRS F-95000 Cergy France

Among random sampling methods, Markov chain Monte Carlo (MC) algorithms are foremost. Using a combination of analytical and numerical approaches, we study their convergence properties toward the steady state, within a random walk Metropolis scheme. Analyzing the relaxation properties of some model algorithms sufficiently simple to enable analytic progress, we show that the deviations from the target steady-state distribution can feature a localization transition as a function of the characteristic length of the attempted jumps defining the random walk. While the iteration of the MC algorithm converges to equilibrium for all choices of jump parameters, the localization transition changes drastically the asymptotic shape of the difference between the probability distribution reached after a finite number of steps of the algorithm and the target equilibrium distribution. We argue that the relaxation before and after the localization transition is respectively limited by diffusion and rejection rates.

关键词： classical Monte Carlo simulations sampling algorithms mixing analysis of algorithms Brownian motion

来源：评论

学校读者我要写书评

暂无评论

Gibbs sampling the posterior of neural networks

引用

JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL 2024年第12期57卷 125002-125002页

作者： Piccioli, Giovanni Troiani, Emanuele Zdeborova, Lenka Ecole Polytech Fed Lausanne EPFL Stat Phys Comp Lab Lausanne Switzerland

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an efficient Gibbs sampler. For small models, the Gibbs sampler attains similar performances as the state-of-the-art Markov chain Monte Carlo methods, such as the Hamiltonian Monte Carlo or the Metropolis adjusted Langevin algorithm, both on real and synthetic data. By framing our analysis in the teacher-student setting, we introduce a thermalization criterion that allows us to detect when an algorithm, when run on data with synthetic labels, fails to sample from the posterior. The criterion is based on the fact that in the teacher-student setting we can initialize an algorithm directly at equilibrium.

关键词： MCMC Bayesian learning neural networks sampling algorithms MCMC thermalization statistical physics

来源：评论

学校读者我要写书评

暂无评论

Imbalanced Learning for Cooperative Spectrum Sensing in Cognitive Radio Networks

Imbalanced Learning for Cooperative Spectrum Sensing in Cogn...

引用

IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Li, Lusi Jiang, He He, Haibo Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

ISBN: (纸本)9781728109626

We propose a novel cooperative spectrum sensing (CSS) framework for cognitive radio networks based on imbalanced learning techniques, which aims to resolve the skewed category distribution problems of signal data. For a radio channel shared by primary users (PUs) and secondary users (SUs), the signal data composed of energy vectors, in which each energy level is estimated by SU, can be used to detect the channel availability via a classifier. However, due to the nature of this application, the existing category-imbalance problem hinders the detection performance since the trained classifier has a better effect on the dominated category. To enhance the performance, sampling (e.g., oversampling, under-sampling, and combination) algorithms are employed to balance the training data set based on the imbalance degree metric of imbalance-ratio. The balanced training set then can be used to train classifiers with initial parameters, and the validation set can be utilized to tune as well as evaluate the classifiers. In the testing phase, the actual desired performance on unseen signal data can be determined based on the testing set, i.e., whether the channel is available or not. The performance of each sampling algorithm is measured in terms of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). The simulation results demonstrate the effectiveness of our proposed framework compared to traditional CSS methods.

关键词： Cognitive radio cooperative spectrum sensing imbalanced learning sampling algorithms detection performance

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：