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Number theoretic correlation inequalities for Dirichlet densities
 Preprint 93060, SFB 343 &quot;Diskrete Strukturen in der Mathematik &quot;, Universitat
, 1993
"... Our main discovery is the inequality D (A; B)D[A;B] DA DB; where A; B are arbitrary sets of positive integers, (A; B) = \Phi (a; b) : a 2 A; b 2 B \Psi is the set of largest common divisors, [A; B] = \Phi [a; b] : a 2 A; b 2 B \Psi is the set of least common multiples, and D denotes the ..."
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Cited by 3 (1 self)
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the lower Dirichlet density. It is much more general than our recent inequality for multiples of sets, which in turn is sharper than Behrend's wellknown inequality. We also extend another recently discovered inequality, which does not seem to have number theoretic predecessors. 1 Introduction
Bayesian Density Estimation and Inference Using Mixtures
 Journal of the American Statistical Association
, 1994
"... We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation, and are exemplified by special cases where data are modelled as a sample from mixtures of normal distributions. Efficien ..."
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Cited by 652 (18 self)
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We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation, and are exemplified by special cases where data are modelled as a sample from mixtures of normal distributions
ModelBased Clustering, Discriminant Analysis, and Density Estimation
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
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Cited by 557 (28 self)
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for modelbased clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, mineeld detection, cluster
article no. NT972072 Number Theoretic Correlation Inequalities for Dirichlet Densities
, 1994
"... Our main discovery is the inequality ..."
Macroscopic strings as heavy quarks in large N gauge theory and Antide Sitter supergravity
 PHYS. J. C22
"... Maldacena has put forward large N correspondence between superconformal field theories on the brane and antide Sitter supergravity in spacetime. We study some aspects of the correspondence between N = 4 superconformal gauge theory on D3brane and maximal supergravity on adS5 × S5 by introducing mac ..."
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Cited by 510 (1 self)
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for macroscopic string in antide Sitter supergravity. As a byproduct we clarify how Polchinski’s Dirichlet and Neumann open string boundary conditions arise. We then study nonBPS macroscopic string antistring pair configuration as physical realization of heavy quark Wilson loop. We obtain Q ¯ Q static
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
 Biometrika
, 1995
"... Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model determi ..."
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Cited by 1330 (24 self)
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Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
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Cited by 826 (88 self)
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to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.
Estimating Continuous Distributions in Bayesian Classifiers
 In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence
, 1995
"... When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality ..."
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Cited by 489 (2 self)
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the normality assumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experimental results on a variety of natural and artificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional
Dynamic topic models
 In ICML
, 2006
"... Scientists need new tools to explore and browse large collections of scholarly literature. Thanks to organizations such as JSTOR, which scan and index the original bound archives of many journals, modern scientists can search digital libraries spanning hundreds of years. A scientist, suddenly ..."
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Cited by 656 (28 self)
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Scientists need new tools to explore and browse large collections of scholarly literature. Thanks to organizations such as JSTOR, which scan and index the original bound archives of many journals, modern scientists can search digital libraries spanning hundreds of years. A scientist, suddenly
Inverse Acoustic and Electromagnetic Scattering Theory, Second Edition
, 1998
"... Abstract. This paper is a survey of the inverse scattering problem for timeharmonic acoustic and electromagnetic waves at fixed frequency. We begin by a discussion of “weak scattering ” and Newtontype methods for solving the inverse scattering problem for acoustic waves, including a brief discussi ..."
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Cited by 1072 (45 self)
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Abstract. This paper is a survey of the inverse scattering problem for timeharmonic acoustic and electromagnetic waves at fixed frequency. We begin by a discussion of “weak scattering ” and Newtontype methods for solving the inverse scattering problem for acoustic waves, including a brief discussion of Tikhonov’s method for the numerical solution of illposed problems. We then proceed to prove a uniqueness theorem for the inverse obstacle problems for acoustic waves and the linear sampling method for reconstructing the shape of a scattering obstacle from far field data. Included in our discussion is a description of Kirsch’s factorization method for solving this problem. We then turn our attention to uniqueness and reconstruction algorithms for determining the support of an inhomogeneous, anisotropic media from acoustic far field data. Our survey is concluded by a brief discussion of the inverse scattering problem for timeharmonic electromagnetic waves. 1.
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