Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets [0.03%]
元克里金法：大型空间数据集的可扩展贝叶斯建模和推理
Rajarshi Guhaniyogi,Sudipto Banerjee
Rajarshi Guhaniyogi
Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations becomes large. There is a burgeoning literature on approaches for analyzing large spatial datasets. In t...
A Geometric Approach to Archetypal Analysis and Nonnegative Matrix Factorization [0.03%]
基于几何的典型分析与非负矩阵分解方法研究
Anil Damle,Yuekai Sun
Anil Damle
Archetypal analysis and non-negative matrix factorization (NMF) are staples in a statisticians toolbox for dimension reduction and exploratory data analysis. We describe a geometric approach to both NMF and archetypal analysis by interpreti...
A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models [0.03%]
基于贝叶斯函数混合模型的结构化声纳地形数据统一分析方法研究
Hongxiao Zhu,Philip Caspers,Jeffrey S Morris et al.
Hongxiao Zhu et al.
Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. While existing analytical approaches often assume independ...
A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data [0.03%]
大数据的贝叶斯分析中的Bootstrap Metropolis-Hastings算法
Faming Liang,Jinsu Kim,Qifan Song
Faming Liang
Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of t...
Elizabeth D Schifano,Jing Wu,Chun Wang et al.
Elizabeth D Schifano et al.
We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterativ...
Shifeng Xiong,Bin Dai,Jared Huling et al.
Shifeng Xiong et al.
We introduce an efficient iterative algorithm, intended for various least squares problems, based on a design of experiments perspective. The algorithm, called orthogonalizing EM (OEM), works for ordinary least squares and can be easily ext...
A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems [0.03%]
退化生物系统的预测模型的迁移学习方法研究
Na Zou,Mustafa Baydogan,Yun Zhu et al.
Na Zou et al.
Modeling of a new domain can be challenging due to scarce data and high-dimensionality. Transfer learning aims to integrate data of the new domain with knowledge about some related old domains, in order to model the new domain better. This ...
Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques [0.03%]
基于群智能技术的两水平饱和设计优化方法研究
Frederick Kin Hing Phoa,Ray-Bing Chen,Weichung Wang et al.
Frederick Kin Hing Phoa et al.
Supersaturated designs (SSDs) are often used to reduce the number of experimental runs in screening experiments with a large number of factors. As more factors are used in the study, the search for an optimal SSD becomes increasingly challe...
MATRIX DISCRIMINANT ANALYSIS WITH APPLICATION TO COLORIMETRIC SENSOR ARRAY DATA [0.03%]
矩阵判别分析及其在色度传感器阵列数据分析中的应用
Wenxuan Zhong,Kenneth S Suslick
Wenxuan Zhong
With the rapid development of nano-technology, a "colorimetric sensor array" (CSA) which is referred to as an optical electronic nose has been developed for the identification of toxicants. Unlike traditional sensors which rely on a single ...
L Brown,A N Donev,A C Bissett
L Brown
We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, ...