Journal Article10.1016/J.CAGEO.2019.06.013
Efficient variography with partition variograms
Júlio Hoffimann,Bianca Zadrozny +1 more
3
TL;DR: This work proposes a generalization of directional variograms to general partitions of spatial data, and introduces a parallel estimation algorithm that can efficiently handle large data sets with more than 105 points.
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About: This article is published in Computers & Geosciences. The article was published on 01 Oct 2019. The article focuses on the topics: Variogram & Estimator.
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Citations
Application of Dueling DQN and DECGA for Parameter Estimation in Variogram Models
Yu Liu,Cong Zhang +1 more
TL;DR: A double elite co-evolutionary genetic algorithm (DECGA) and deep reinforcement learning (dueling DQN) was introduced to estimate the parameters of variogram single or nested models so as to achieve better generalization performance.
Peer Review
Geostatistical Learning: Challenges and Opportunities (cid:63)
TL;DR: The geostatistical (transfer) learning problem is introduced, and the challenges of learning from geospatial data are illustrated by assessing widely-used methods for estimating generalization error of learning models, under covariate shift and spatial correlation.
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Introduction to Algorithms
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TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
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MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
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TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.