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Numerical Evaluation of Gamma Inequalities
, 2006
"... This paper addresses the problem of numerically evaluating the ..."
Evaluating the Accuracy of SamplingBased Approaches to the Calculation of Posterior Moments
 IN BAYESIAN STATISTICS
, 1992
"... Data augmentation and Gibbs sampling are two closely related, samplingbased approaches to the calculation of posterior moments. The fact that each produces a sample whose constituents are neither independent nor identically distributed complicates the assessment of convergence and numerical accurac ..."
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Cited by 604 (12 self)
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accuracy of the approximations to the expected value of functions of interest under the posterior. In this paper methods from spectral analysis are used to evaluate numerical accuracy formally and construct diagnostics for convergence. These methods are illustrated in the normal linear model
ON THE NUMERICAL EVALUATION OF FREDHOLM DETERMINANTS
, 2008
"... Some significant quantities in mathematics and physics are most naturally expressed as the Fredholm determinant of an integral operator, most notably many of the distribution functions in random matrix theory. Though their numerical values are of interest, there is no systematic numerical treatment ..."
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Cited by 43 (6 self)
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treatment of Fredholm determinants to be found in the literature. Instead, the few numerical evaluations that are available rely on eigenfunction expansions of the operator, if expressible in terms of special functions, or on alternative, numerically more straightforwardly accessible analytic expressions, e
Do Better Schools Matter? Parental Valuation of Elementary Education
 QUARTERLY JOURNAL OF ECONOMICS
, 1999
"... The evaluation of numerous school reforms requires an understanding of the value of better schools. Given the difficulty of calculating the relationship between school quality and student outcomes, I turn to another method and use house prices to infer the value parents place on school quality. I lo ..."
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Cited by 500 (3 self)
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The evaluation of numerous school reforms requires an understanding of the value of better schools. Given the difficulty of calculating the relationship between school quality and student outcomes, I turn to another method and use house prices to infer the value parents place on school quality. I
Detecting faces in images: A survey
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... Images containing faces are essential to intelligent visionbased human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image se ..."
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Cited by 839 (4 self)
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of its threedimensional position, orientation, and the lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose
Pastry: Scalable, decentralized object location and routing for largescale peertopeer systems
 IN PROC. OF THE 18TH IFIP/ACM INTERNATIONAL CONFERENCE ON DISTRIBUTED SYSTEMS PLATFORMS,
, 2001
"... This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing substrate for widearea peertopeer applications. Pastry performs applicationlevel routing and object location in a potentially very large overlay network of nodes connected via the Intern ..."
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Cited by 1932 (1 self)
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This paper presents the design and evaluation of Pastry, a scalable, distributed object location and routing substrate for widearea peertopeer applications. Pastry performs applicationlevel routing and object location in a potentially very large overlay network of nodes connected via
LucasKanade 20 Years On: A Unifying Framework: Part 3
 International Journal of Computer Vision
, 2002
"... Since the LucasKanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision. Applications range from optical flow, tracking, and layered motion, to mosaic construction, medical image registration, and face coding. Numerous algorithms hav ..."
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Cited by 706 (30 self)
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Since the LucasKanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision. Applications range from optical flow, tracking, and layered motion, to mosaic construction, medical image registration, and face coding. Numerous algorithms
Numerical Evaluation of Harmonic Polylogarithms
, 2001
"... Harmonic polylogarithms H(a; x), a generalization of Nielsenâ€™s polylogarithms Sn,p(x), appear frequently in analytic calculations of radiative corrections in quantum field theory. We present an algorithm for the numerical evaluation of harmonic polylogarithms of arbitrary real argument. This algorit ..."
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Harmonic polylogarithms H(a; x), a generalization of Nielsenâ€™s polylogarithms Sn,p(x), appear frequently in analytic calculations of radiative corrections in quantum field theory. We present an algorithm for the numerical evaluation of harmonic polylogarithms of arbitrary real argument
Numerical Evaluation of Special Functions
 In W. Gautschi (Ed.), AMS Proceedings of Symposia in Applied Mathematics 48
, 1994
"... . This document is an excerpt from the current hypertext version of an article that appeared in Walter Gautschi (ed.), Mathematics of Computation 19431993: A HalfCentury of Computational Mathematics, Proceedings of Symposia in Applied Mathematics 48, American Mathematical Society, Providence, ..."
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Cited by 28 (0 self)
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in investigations by engineers, mathematicians, scientists and statisticians. The purpose of this paper is to assist in locating useful approximations and software for the numerical generation of these functions, and to offer some suggestions for future developments in this field. 5.9. Mathieu, Lam
Sequential minimal optimization: A fast algorithm for training support vector machines
 Advances in Kernel MethodsSupport Vector Learning
, 1999
"... This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possi ..."
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Cited by 461 (3 self)
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possible QP problems. These small QP problems are solved analytically, which avoids using a timeconsuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because matrix computation
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