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633
Development of an Equation of State for an Arbitrary Mixture of Thermally Perfect Gases to the FINFLO Flow Solver
, 1995
"... In this report, we describe the extension of an existing NavierStokes solver to enable the implementation of an arbitrary equation of state. The development of an equation of state for an arbitrary mixture of thermally perfect gases is outlined. Also, a general scalar convectiondiffusion solver is ..."
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Cited by 1 (0 self)
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In this report, we describe the extension of an existing NavierStokes solver to enable the implementation of an arbitrary equation of state. The development of an equation of state for an arbitrary mixture of thermally perfect gases is outlined. Also, a general scalar convectiondiffusion solver
Modeling of the Equation of State for an Arbitrary Mixture of Thermally Perfect Gases in Computational Fluid Dynamics
, 1995
"... ..."
Learning Mixtures of Arbitrary Gaussians
 STOC
, 2001
"... Mixtures of gaussian (or normal) distributions arise in a variety of application areas. Many techniques have been proposed for the task of finding the component gaussians given samples from the mixture, such as the EM algorithm, a localsearch heuristic from Dempster, Laird and Rubin (1977). However ..."
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Cited by 91 (6 self)
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Mixtures of gaussian (or normal) distributions arise in a variety of application areas. Many techniques have been proposed for the task of finding the component gaussians given samples from the mixture, such as the EM algorithm, a localsearch heuristic from Dempster, Laird and Rubin (1977
Entanglement of Formation of an Arbitrary State of Two Qubits
, 1998
"... The entanglement of a pure state of a pair of quantum systems is defined as the entropy of either member of the pair. The entanglement of formation of a mixed state ρ is defined as the minimum average entanglement of a set of pure states constituting a decomposition of ρ. An earlier paper [Phys. Rev ..."
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Cited by 200 (0 self)
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. Rev. Lett. 78, 5022 (1997)] conjectured an explicit formula for the entanglement of formation of a pair of binary quantum objects (qubits) as a function of their density matrix, and proved the formula to be true for a special class of mixed states. The present paper extends the proof to arbitrary
Pachinko allocation: DAGstructured mixture models of topic correlations
 In Proceedings of the 23rd International Conference on Machine Learning
, 2006
"... Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization and manifold discovery in discrete data. However, LDA does not capture correlations between topics. In this paper, we introduce the pachinko allocation model (PAM), which captures arbitr ..."
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Cited by 181 (8 self)
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arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). The leaves of the DAG represent individual words in the vocabulary, while each interior node represents a correlation among its children, which may be words or other interior nodes (topics). PAM provides
Efficiently Learning Mixtures of Two Arbitrary Gaussians
, 2010
"... Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We provide a polynomialtime algorithm for this problem for the case of two Gaussians in n dimensions (even if they overlap), with provably minimal assumptions on the Gaussian ..."
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second consequence is a polynomialtime density estimation algorithm for arbitrary mixtures of two Gaussians, generalizing previous work on axisaligned Gaussians (Feldman et al, 2006).
Supervised learning from incomplete data via an EM approach
 Advances in Neural Information Processing Systems 6
, 1994
"... Realworld learning tasks may involve highdimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data sets. We use mixture models for the density estimates and make two distinct appeal ..."
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Cited by 232 (2 self)
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Realworld learning tasks may involve highdimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data sets. We use mixture models for the density estimates and make two distinct
The spectral method for general mixture models
 18th Annual Conference on Learning Theory (COLT
, 2005
"... Abstract. We present an algorithm for learning a mixture of distributions based on spectral projection. We prove a general property of spectral projection for arbitrary mixtures and show that the resulting algorithm is efficient when the components of the mixture are logconcave distributions in!n wh ..."
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Cited by 61 (8 self)
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Abstract. We present an algorithm for learning a mixture of distributions based on spectral projection. We prove a general property of spectral projection for arbitrary mixtures and show that the resulting algorithm is efficient when the components of the mixture are logconcave distributions in
Toward Learning Gaussian Mixtures with Arbitrary Separation
"... In recent years analysis of complexity of learning Gaussian mixture models from sampled data has received significant attention in computational machine learning and theory communities. In this paper we present the first result showing that polynomial time learning of multidimensional Gaussian Mixtu ..."
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Cited by 4 (1 self)
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In recent years analysis of complexity of learning Gaussian mixture models from sampled data has received significant attention in computational machine learning and theory communities. In this paper we present the first result showing that polynomial time learning of multidimensional Gaussian
Learning Mixtures of Arbitrary Gaussians (Extended Abstract)
 STOC'01
, 2001
"... Mixtures of gaussian (or normal) distributions arise in a variety of application areas. Many techniques have been proposed for the task of finding the component gaussians given samples from the mixture, such as the EM algorithm, a localsearch heuristic from Dempster, Laird and Rubin (1977). However ..."
Abstract
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Mixtures of gaussian (or normal) distributions arise in a variety of application areas. Many techniques have been proposed for the task of finding the component gaussians given samples from the mixture, such as the EM algorithm, a localsearch heuristic from Dempster, Laird and Rubin (1977
Results 1  10
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633