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Mixture model

About: Mixture model is a(n) research topic. Over the lifetime, 18155 publication(s) have been published within this topic receiving 588317 citation(s).

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Papers
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Open accessBook
Christopher M. Bishop1Institutions (1)
17 Aug 2006-
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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Topics: Kernel method (60%), Kernel (statistics) (60%), Graphical model (58%) ...read more

22,762 Citations


Open accessJournal ArticleDOI: 10.1214/SS/1177011136
Abstract: The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give misleading answers. Our methods are simple and generally applicable to the output of any iterative simulation; they are designed for researchers primarily interested in the science underlying the data and models they are analyzing, rather than for researchers interested in the probability theory underlying the iterative simulations themselves. Our recommended strategy is to use several independent sequences, with starting points sampled from an overdispersed distribution. At each step of the iterative simulation, we obtain, for each univariate estimand of interest, a distributional estimate and an estimate of how much sharper the distributional estimate might become if the simulations were continued indefinitely. Because our focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normality after transformations and marginalization, we derive our results as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations. The methods are illustrated on a random-effects mixture model applied to experimental measurements of reaction times of normal and schizophrenic patients.

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Topics: Bayesian inference (58%), Gibbs sampling (57%), Mixture model (55%) ...read more

12,022 Citations


Open access
Christopher M. Bishop1Institutions (1)
01 Jan 2006-
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

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Topics: Kernel method (60%), Kernel (statistics) (60%), Graphical model (58%) ...read more

10,141 Citations


Journal ArticleDOI: 10.1109/MSP.2012.2205597
Geoffrey E. Hinton1, Li Deng2, Dong Yu2, George E. Dahl1  +7 moreInstitutions (4)
Abstract: Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.

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Topics: Acoustic model (62%), Time delay neural network (60%), FMLLR (60%) ...read more

7,700 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202213
2021993
20201,126
20191,180
20181,119
20171,156

Top Attributes

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Topic's top 5 most impactful authors

Nizar Bouguila

268 papers, 4K citations

Geoffrey J. McLachlan

112 papers, 7.4K citations

Paul D. McNicholas

85 papers, 3K citations

Wentao Fan

58 papers, 619 citations

David B. Dunson

45 papers, 1.5K citations

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