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Statistics for spatial data

TLDR
In this paper, the authors present a survey of statistics for spatial data in the field of geostatistics, including spatial point patterns and point patterns modeling objects, using Lattice Data and spatial models on lattices.
Abstract
Statistics for Spatial Data GEOSTATISTICAL DATA Geostatistics Spatial Prediction and Kriging Applications of Geostatistics Special Topics in Statistics for Spatial Data LATTICE DATA Spatial Models on Lattices Inference for Lattice Models SPATIAL PATTERNS Spatial Point Patterns Modeling Objects References Author Index Subject Index.

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Gaussian Processes for Machine Learning

TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Journal ArticleDOI

Local Indicators of Spatial Association—LISA

TL;DR: In this paper, a new general class of local indicators of spatial association (LISA) is proposed, which allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation.
Journal ArticleDOI

Efficient Global Optimization of Expensive Black-Box Functions

TL;DR: This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.

Active Learning Literature Survey

Burr Settles
TL;DR: This report provides a general introduction to active learning and a survey of the literature, including a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date.
Journal ArticleDOI

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

TL;DR: This work considers approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non‐Gaussian response variables and can directly compute very accurate approximations to the posterior marginals.