首页/论文/2012.08036

arXiv 2012.080362020-12stat.MLcs.LGstat.AP

Applications of multivariate quasi-random sampling with neural networks

University of Waterloo（滑铁卢大学）

Marius Hofert, Avinash Prasad, Mu Zhu

备注：: 17 pages, 5 figures

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

Generative moment matching networks (GMMNs) are suggested for modeling the cross-sectional dependence between stochastic processes. The stochastic processes considered are geometric Brownian motions and ARMA-GARCH models. Geometric Brownian motions lead to an application of pricing American basket call options under dependence and ARMA-GARCH models lead to an application of simulating predictive distributions. In both types of applications the benefit of using GMMNs in comparison to parametric dependence models is highlighted and the fact that GMMNs can produce dependent quasi-random samples with no additional effort is exploited to obtain variance reduction.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data

Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）

With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing. To reduce the storage footprint and facilitate scalable post-hoc analyses of such scientific data sets, various data reduction/summarization methods have been proposed over the years. Different flavors of sampling algorithms exist to sample the high-resolution scientific data, while preserving important data properties required for subsequent analyses. However, most of these sampling algorithms are designed for univariate data and cater to post-hoc analyses…

2020-08-31cs.GRcs.HCcs.LG

一种面向线性截断多元正态分布的快速鲁棒椭圆切片采样实现

A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions

University of Pennsylvania（宾夕法尼亚大学）

针对线性截断多元正态分布采样难题，本文提出可在$\mathcal{O}(m \log m)$时间内计算椭圆与多面体交集的椭圆切片采样算法，提升了数值稳定性、运行速度与并行能力。

2024-07-15cs.LGstat.ML

Quasi-random sampling for multivariate distributions via generative neural networks

University of Waterloo（滑铁卢大学）

Generative moment matching networks (GMMNs) are introduced for generating quasi-random samples from multivariate models with any underlying copula in order to compute estimates under variance reduction. So far, quasi-random sampling for multivariate distributions required a careful design, exploiting specific properties (such as conditional distributions) of the implied parametric copula or the underlying quasi-Monte Carlo (QMC) point set, and was only tractable for a small number of models. Utilizing GMMNs allows one to construct quasi-random samples for a much larger variety of multivariate …

2020-04-02cs.LGstat.ML

基于多元伯努利的采样方法用于多标签数据及其在元研究中的应用

A Multivariate Bernoulli-Based Sampling Method for Multi-Label Data with Application to Meta-Research

Department of Biostatistics, University of Arkansas for Medical Sciences（生物统计学系，亚拉巴马州医学科学大学）；Arkansas Children’s Research Institute（亚拉巴马州儿童研究研究所）；Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington（流行病学与生物统计学系，印第安纳大学公共健康学院-布卢明顿分校）；Department of Psychology, Emory University（心理学系，埃默里大学）

针对多标签数据中标签频率差异大且存在依赖关系的问题，提出一种基于多元伯努利分布的加权采样算法，通过估计标签组合权重实现目标分布特征，并在Web of Science研究文章数据上验证了其增强少数类别代表性的效果。

2026-09-03cs.LGstat.ML

End-to-End Learning for the Deep Multivariate Probit Model

Cornell University（康奈尔大学）

The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP model, given that its likelihood involves integrating over a multidimensional constrained space of latent variables, significantly limits its application in practice. We propose a flexible deep generalization of the classic MVP, the Deep Multivariate Probit Model (DMVP), which is an end-to-end learning scheme that uses an efficient parallel sampling process of the multivariate probit model to exploit GPU-boosted deep ne…

2018-07-13cs.LGstat.ML

Infinite Mixtures of Multivariate Gaussian Processes

East China Normal University（华东师范大学）

This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning. As an extension of the single multivariate Gaussian process, the mixture model has the advantages of modeling multimodal data and alleviating the computationally cubic complexity of the multivariate Gaussian process. A Dirichlet process prior is adopted to allow the (possibly infinite) number of mixture components to be automatically inferred from training data, and Markov chain Monte Carlo sampling techniques are use…

2013-07-26cs.LGstat.ML

更多相关论文收起更多论文

Quasi-parametric rates for Sparse Multivariate Functional Principal Components Analysis

This work aims to give non-asymptotic results for estimating the first principal component of a multivariate random process. We first define the covariance function and the covariance operator in the multivariate case. We then define a projection operator. This operator can be seen as a reconstruction step from the raw data in the functional data analysis context. Next, we show that the eigenelements can be expressed as the solution to an optimization problem, and we introduce the LASSO variant of this optimization problem and the associated plugin estimator. Finally, we assess the estimator's…

2022-12-19math.STstat.MEstat.ML

基于多项式混沌展开与多元主动学习的工程结构不确定性量化

Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning

Brno University of Technology（布尔诺理工大学）；University of Rostock（罗斯托克大学）

针对多输出工程问题中单一实验设计难以同时准确近似所有输出量的问题，提出一种自适应序贯采样方法，通过平衡输入空间探索与多输出聚合方差信息，构建多项式混沌展开代理模型，数值实验表明该方法提高了代理精度和稳定性。

2026-06-17cs.LGstat.ML

Sensor selection on graphs via data-driven node sub-sampling in network time series

Université de Bordeaux（波尔多大学）

This paper is concerned by the problem of selecting an optimal sampling set of sensors over a network of time series for the purpose of signal recovery at non-observed sensors with a minimal reconstruction error. The problem is motivated by applications where time-dependent graph signals are collected over redundant networks. In this setting, one may wish to only use a subset of sensors to predict data streams over the whole collection of nodes in the underlying graph. A typical application is the possibility to reduce the power consumption in a network of sensors that may have limited battery…

2020-04-24cs.LGstat.ML

Elliptical slice sampling

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorith…

2010-03-19stat.COstat.ML

Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization

We consider stochastic zero-order optimization problems, which arise in settings from simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients of a stochastic function using finite differences within a common random number framework. We employ modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations. We provide preliminary numerical experiments to illustrate potential performance benefits of the proposed method.

2019-10-29math.OCstat.ML