The Finite Mixture Model (FMM)
Cluster Analysis of Multivariate Data Using Scaled Dirichlet Finite Mixture Model
... 2.1.2 Finite Mixture Models (FMM) From the previous section, we mentioned a robust technique for better modeling ...a mixture model that can represent multi-component distributions underlying ...
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Using a Finite Mixture Model-Based Clustering Approach to Identify Move and Wait Strategies.
... ABSTRACT SCHOLCOVER, FEDERICO. Using a Finite Mixture Model-Based Clustering Approach to Identify Move and Wait Strategies (Under the direction of Douglas J. Gillan). Telerobotic platforms can be ...
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Finite mixture model approach applied in the analysis of a turbulent bistable flow on two rows tube bank
... A finite mixture model tool is applied, where an expectation-maximization algorithm performs the maximum likelihood estimation according to a known PDF with aid of a Monte Carlo method, to know what ...
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Genetic algorithms for finite mixture model based tissue classification
... [email protected] Abstract: Finite mixture models (FMMs) are an indis- pensable tool for unsupervised classification in brain imaging. Fitting a FMM to the data leads to a com- plex optimization problem. ...
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Topic Analysis Using a Finite Mixture Model
... Ex- perimental results indicate, :however, that our method significantly outper~brms such a com- bined m e t h o d in topic identification and out- performs it in te[r] ...
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Document Classification Using a Finite Mixture Model
... 3We calculate the probabilities here by using the so- called expected likelihood estimator (Gale and Church, 1990):.. By assigning words to clusters, it can drastica[r] ...
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Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model
... In cluster 1, the enriched elements include Mo, As, Cd, Sb, S, Bi, Li, Cr, Ni, V, Cu, Zn, Sc, Co, Fe, and P ( Fig. 5 a). These elements are all commonly enriched in shales and other fine-grained marine sedi- mentary rocks ...
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Classification of Glioblastoma Multiforme Patients Based on an Integrative Multi-Layer Finite Mixture Model System
... the model fitting showed that variables with larger vari- ances were over-selected by the penalized model, because of this the layers were scaled using the min-max ...
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Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion
... Here we focus more on the role of the hyperparameters in determining the final GCICL solution, essentially providing a sensitivity analysis. In particular we make use of simulated data t[r] ...
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Finite mixture models and model-based clustering
... Hierarchical model-based clustering and cluster merging A fundamental issue with finite mixture modeling is that finding the best fit- ting mixture is not necessarily equivalent to finding the ...
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Finite Mixture of Heteroscedastic Single Index Models
... Algorithm; Finite Mixture Model; Heterogeneity; Heteroscedasticity; Local Linear Smoothing; Single-Index Model ...parametric model due to the lack of enough prior knowledge, researchers ...
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An algorithm for unsupervised learning and optimization of finite mixture models
... the model selected using these criteria is not necessarily the best model for clustering small data ...the model components [17] ...the mixture complexity is a quadratic function of the number ...
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Bayesian Finite Mixture Negative Binomial Model for Over-dispersed Count Data with Application to DMFT Index Data
... Poisson model is used [5-6], due to existence of overdispersion mainly due to generation of excess ...Poisson model could be a viable alternative to the joint modeling of excess of zeros and over- ...
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A Poisson Stochastic Frontier Model with Finite Mixture Structure
... Keywords: efficiency, Poisson stochastic frontier, mixture, innovation, states JEL: C13, C24, O33, O51 1. Introduction Estimation of productive efficiency is performed in the framework of frontier methodologies, ...
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A Poisson Stochastic Frontier Model with Finite Mixture Structure
... Keywords: efficiency, Poisson stochastic frontier, mixture, innovation, states JEL: C13, C24, O33, O51 1. Introduction Estimation of productive efficiency is performed in the framework of frontier methodologies, ...
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Variational Approximations in Bayesian Model Selection for Finite Mixture Distributions
... for finite mixture models and, in particular, we shall show how variational methods can be used to deter- mine a suitable number of components in the case of a mixture of Gaussian ...
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Bayesian Prediction under a Finite Mixture of Generalized Exponential Lifetime Model
... [email protected] Abstract In this article a heterogeneous population is represented by a mixture of two generalized exponential distributions. Using the two-sample prediction technique, Bayesian ...
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A finite mixture latent trajectory model for hirings and separations in the labor market
... Finite mixture latent trajectory model 3 The single CC represents the unit of observation for a total of 937,123 records. In order to avoid a possible distortion due to new-born firms in the period ...
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Estimation of Finite Mixture Models
... class is the most accurate. The set theoretic estimates guarantee that a solution will satisfy the finite mixture model set as well as the sum-to-one and nonnegativity constraints. The SPOCS estimate is not ...
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IMAGE SEGMENTATION BY USING FINITE BIVARIATE DOUBLY TRUNCATED GAUSSIAN MIXTURE MODEL
... however it is highly important that another character of the pixel, namely, brightness plays a dominant role in Image Retrieval. It is also to be observed that the pixel intensity and brightness are correlated and ...
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