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66
Prediction With Gaussian Processes: From Linear Regression To Linear Prediction And Beyond
 Learning and Inference in Graphical Models
, 1997
"... The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. Th ..."
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Cited by 195 (4 self)
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The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads in to a more general discussion of Gaussian processes in section 4. Section 5 deals with further issues, including hierarchical modelling and the setting of the parameters that control the Gaussian process, the covariance functions for neural network models and the use of Gaussian processes in classification problems. PREDICTION WITH GAUSSIAN PROCESSES: FROM LINEAR REGRESSION TO LINEAR PREDICTION AND BEYOND 2 1 Introduction In the last decade neural networks have been used to tackle regression and classification problems, with some notable successes. It has also been widely recognized that they form a part of a wide variety of nonlinear statistical techniques that can be used for...
Bayesian Classification with Gaussian Processes
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... We consider the problem of assigning an input vector x to one of m classes by predicting P (cjx) for c = 1; : : : ; m. For a twoclass problem, the probability of class 1 given x is estimated by oe(y(x)), where oe(y) = 1=(1 + e ). A Gaussian process prior is placed on y(x), and is combined wi ..."
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Cited by 130 (1 self)
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We consider the problem of assigning an input vector x to one of m classes by predicting P (cjx) for c = 1; : : : ; m. For a twoclass problem, the probability of class 1 given x is estimated by oe(y(x)), where oe(y) = 1=(1 + e ). A Gaussian process prior is placed on y(x), and is combined with the training data to obtain predictions for new x points.
Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
, 1997
"... Abstract. Gaussian processes are a natural way of defining prior distributions over functions of one or more input variables. In a simple nonparametric regression problem, where such a function gives the mean of a Gaussian distribution for an observed response, a Gaussian process model can easily be ..."
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Cited by 121 (1 self)
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Abstract. Gaussian processes are a natural way of defining prior distributions over functions of one or more input variables. In a simple nonparametric regression problem, where such a function gives the mean of a Gaussian distribution for an observed response, a Gaussian process model can easily be implemented using matrix computations that are feasible for datasets of up to about a thousand cases. Hyperparameters that define the covariance function of the Gaussian process can be sampled using Markov chain methods. Regression models where the noise has a t distribution and logistic or probit models for classification applications can be implemented by sampling as well for latent values underlying the observations. Software is now available that implements these methods using covariance functions with hierarchical parameterizations. Models defined in this way can discover highlevel properties of the data, such as which inputs are relevant to predicting the response. 1
Modelbased Geostatistics
 Applied Statistics
, 1998
"... Conventional geostatistical methodology solves the problem of predicting the realised value of a linear functional of a Gaussian spatial stochastic process, S(x), based on observations Y i = S(x i ) + Z i at sampling locations x i , where the Z i are mutually independent, zeromean Gaussian random v ..."
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Cited by 96 (4 self)
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Conventional geostatistical methodology solves the problem of predicting the realised value of a linear functional of a Gaussian spatial stochastic process, S(x), based on observations Y i = S(x i ) + Z i at sampling locations x i , where the Z i are mutually independent, zeromean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly inappropriate. The first concerns the assessment of residual contamination from nuclear weapons testing on a South Pacific island, in which the sampling method generates spatially indexed Poisson counts conditional on an unobserved spatially varying intensity of radioactivity; we conclude that a coventional geostatistical analysis oversmooths the data and underestimates the spatial extremes of the intensity. The second application provides a description of spatial variation in the risk of campylobacter infections relative to other enteric infections in part of North Lancashire and South C...
A generalized moments estimator for the autoregressive parameter in a spatial model
 International Economic Review
, 1999
"... This paper is concerned with the estimation of the autoregressive parameter in a widely considered spatial autocorrelation model. The typical estimator for this parameter considered in the literature is the (quasi) maximum likelihood estimator corresponding to a normal density. However, as discussed ..."
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Cited by 95 (12 self)
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This paper is concerned with the estimation of the autoregressive parameter in a widely considered spatial autocorrelation model. The typical estimator for this parameter considered in the literature is the (quasi) maximum likelihood estimator corresponding to a normal density. However, as discussed in the paper, the (quasi) maximum likelihood estimator may not be computationally feasible in many cases involving moderate or large sized samples. In this paper we suggest a generalized moments estimator that is computationally simple irrespective of the sample size. We provide results concerning the large and small sample properties of this estimator. 1 Introduction 1 There exists a large body of literature that considers autocorrelation of the disturbances across cross sectional units for panel data, i.e., data which are observed both across cross sectional units and over time. However, the estimation of models that permit for autocorrelation of the disturbances across
Computer Experiments
, 1996
"... Introduction Deterministic computer simulations of physical phenomena are becoming widely used in science and engineering. Computers are used to describe the flow of air over an airplane wing, combustion of gasses in a flame, behavior of a metal structure under stress, safety of a nuclear reactor, a ..."
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Cited by 67 (5 self)
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Introduction Deterministic computer simulations of physical phenomena are becoming widely used in science and engineering. Computers are used to describe the flow of air over an airplane wing, combustion of gasses in a flame, behavior of a metal structure under stress, safety of a nuclear reactor, and so on. Some of the most widely used computer models, and the ones that lead us to work in this area, arise in the design of the semiconductors used in the computers themselves. A process simulator starts with a data structure representing an unprocessed piece of silicon and simulates the steps such as oxidation, etching and ion injection that produce a semiconductor device such as a transistor. A device simulator takes a description of such a device and simulates the flow of current through it under varying conditions to determine properties of the device such as its switching speed and the critical voltage at which it switches. A circuit simulator takes a list of devices and the
Spatial Econometrics
 PALGRAVE HANDBOOK OF ECONOMETRICS: VOLUME 1, ECONOMETRIC THEORY
, 2001
"... Spatial econometric methods deal with the incorporation of spatial interaction and spatial structure into regression analysis. The field has seen a recent and rapid growth spurred both by theoretical concerns as well as by the need to be able to apply econometric models to emerging large geocoded da ..."
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Cited by 64 (5 self)
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Spatial econometric methods deal with the incorporation of spatial interaction and spatial structure into regression analysis. The field has seen a recent and rapid growth spurred both by theoretical concerns as well as by the need to be able to apply econometric models to emerging large geocoded data bases. The review presented in this chapter outlines the basic terminology and discusses in some detail the specification of spatial effects, estimation of spatial regression models, and specification tests for spatial effects.
Estimation of highdimensional prior and posterior covariance matrices in Kalman filter variants
 Journal of Multivariate Analysis
, 2007
"... This work studies the effect of using Monte Carlo based methods to estimate highdimensional systems. Recent focus in the geosciences has been on representing the atmospheric state using a probability density function, and, for extremely highdimensional systems, various sample based Kalman filter t ..."
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Cited by 47 (5 self)
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This work studies the effect of using Monte Carlo based methods to estimate highdimensional systems. Recent focus in the geosciences has been on representing the atmospheric state using a probability density function, and, for extremely highdimensional systems, various sample based Kalman filter techniques have been developed to address the problem of realtime assimilation of system information and observations. As the employed sample sizes are typically several orders of magnitude smaller than the system dimension, such sampling techniques inevitably induces considerable variability into the state estimate, primarily through prior and posterior sample covariance matrices. In this article we quantify this variability with mean squared error measures for two MonteCarlo based Kalman filter variants, the ensemble Kalman filter and the squareroot filter. Under weak assumptions, we derive exact expressions of the error measures. In other cases, we rely on matrix expansions and provide approximations. We show that covarianceshrinking (tapering) based on the Schur product of the prior sample covariance matrix and a positive definite function is a simple, computationally feasible, and very effective technique to reduce sample variability and to address rankdeficient sample covariances. We propose practical rules for obtaining optimally tapered sample covariance matrices. The theoretical results are verified and illustrated with extensive simulations.
Asymptotic distributions of quasimaximum likelihood estimates for spatial autoregressive models. Econometrica
, 2004
"... This paper investigates asymptotic properties of the maximim likelihood estimator and the quasimaximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model. It is import ..."
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Cited by 45 (7 self)
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This paper investigates asymptotic properties of the maximim likelihood estimator and the quasimaximum likelihood estimator for the spatial autoregressive model. The rates of convergence of those estimators may depend on some general features of the spatial weights matrix of the model. It is important to make the distinction with different spatial scenarios. Under the scenario that each unit will be influenced by only a few neighboring units, the estimators may have √ nrate of convergence and be asymptotic normal. When each unit can be influenced by many neighbors, irregularity of the information matrix may occur and various components of the estimators may have different rates of convergence.
Under the hood: issues in the specification and interpretation of spatial regression models
 Agricultural Economics
, 2002
"... This paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial ” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theorydriven as well as from a datadriven persp ..."
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Cited by 44 (1 self)
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This paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial ” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theorydriven as well as from a datadriven perspective. Considerable attention is paid to the inferential framework necessary to carry out estimation and testing and the different assumptions, constraints and implications embedded in the various specifications available in the literature. The review combines insights from the traditional spatial econometrics literature as well as from geostatistics, biostatistics and medical image analysis.