Results 231 to 240 of about 736,978 (266)

Design and Modeling Strategies for Mixture-of-Mixtures Experiments

Technometrics, 2011
In mixture-of-mixtures experiments major components are defined as the components which themselves are mixtures of some other components, called minor components. Sometimes components are divided into different categories where each category is called a major component and the components within a major component become minor components.
Lulu Kang, V. Roshan Joseph, William A. Brenneman   +2 more
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Mixture of mixture n-gram language models

2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 2013
This paper presents a language model adaptation technique to build a single static language model from a set of language models each trained on a separate text corpus while aiming to maximize the likelihood of an adaptation data set given as a development set of sentences.
Hasim Sak, Cyril Allauzen, Kaisuke Nakajima, Françoise Beaufays   +3 more
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Multilevel Mixture Factor Models

Multivariate Behavioral Research, 2012
Factor analysis is a statistical method for describing the associations among sets of observed variables in terms of a small number of underlying continuous latent variables. Various authors have proposed multilevel extensions of the factor model for the analysis of data sets with a hierarchical structure.
Varriale R., Vermunt J. K.
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Finite Mixture Models for Proportions

Biometrics, 1997
Six data sets recording fetal control mortality in mouse litters are presented. The data are clearly overdispersed, and a standard approach would be to describe the data by means of a beta-binomial model or to use quasi-likelihood methods. For five of the examples, we show that beta-binomial model provides a reasonable description but that the fit can ...
Brooks, SP, Morgan, BJ, Ridout, MS, Pack, SE   +3 more
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Latent Dirichlet mixture model

Neurocomputing, 2018
Text representation based on latent topic model is seen as a non-Gaussian problem where the observed words and latent topics are multinomial variables and the topic proportionals are Dirichlet variables. Traditional topic model is established by introducing a single Dirichlet prior to characterize the topic proportionals.
Jen-Tzung Chien, Chao-Hsi Lee, Zheng-Hua Tan   +2 more
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Mixture Models for Classification

2007
Finite mixture distributions provide efficient approaches of model-based clustering and classification. The advantages of mixture models for unsupervised classification are reviewed. Then, the article is focusing on the model selection problem. The usefulness of taking into account the modeling purpose when selecting a model is advocated in the ...
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Fuzzy Gaussian Mixture Models

Pattern Recognition, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhaojie Ju, Honghai Liu 0001
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SMEM Algorithm for Mixture Models

Neural Computation, 2000
We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of the space.
Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton   +3 more
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ON MIXTURE MEMORY GARCH MODELS

Journal of Time Series Analysis, 2013
We propose a new volatility model, which is called the mixture memory generalized autoregressive conditional heteroskedasticity (MM‐GARCH) model. The MM‐GARCH model has two mixture components, of which one is a short‐memory GARCH and the other is the long‐memory fractionally integrated GARCH.
Li, M, Li, WK, Li, G
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Jeffreys prior for mixture models [PDF]

, 2014
Mixture models may be a useful and flexible tool to describe data with a complicated structure, for instance characterized by multimodality or asymmetry. In a Bayesian setting, it is a well established fact that one need to be careful in using improper prior distributions, since the posterior distribution may not be proper.
GRAZIAN, CLARA, C. P. Robert
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