Search Results - "Expectation–maximization algorithm"
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Improved expectation maximization algorithm for Gaussian mixed model using the kernel method
Published 2013“…We look at several issues encountered when calculating the maximum likelihood estimates of the Gaussian mixed model using an Expectation Maximization algorithm. Firstly, we look at a mechanism for the determination of the initial number of Gaussian components and the choice of the initial values of the algorithm using the kernel method. …”
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The performance of expectation maximization (EM) algorithm in Gaussian Mixed Models (GMM)
Published 2009Subjects: “…Expectation-maximization algorithms.…”
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Improving multi-criteria decision making algorithm for material selection
Published 2011Subjects: “…Expectation-maximization algorithms…”
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Clustering for point pattern data
Published 2017“…The second is a model-based approach, formulated via random finite set theory, and solved by the Expectation-Maximization algorithm. Numerical experiments show that the proposed methods perform well on both simulated and real data.…”
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Reservoir and lithofacies shale classification based on NMR logging
Published 2020“…Thus, in this paper, NMR response curves (of shale samples) were rigorously mathematically analyzed (with an Expectation Maximization algorithm) and categorized based on the NMR data and their geology, respectively. …”
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Fraud detection in telecommunication industry using Gaussian mixed model
Published 2013“…In this article, we propose a new fraud detection algorithm using Gaussian mixed model (GMM), a probabilistic model successfully used in speech recognition problem. The expectation maximization algorithm is used to estimate the parameter of the model such that the initial values of the algorithm is determined using the kernel method. …”
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The detection of multiple faults in a Bayesian setting using dynamic programming approaches
Published 2020“…These methods use iterative approximations of MAP estimates, via the application of an iterative Expectation–Maximization algorithm technique. Numerical simulations are conducted and analysed to evaluate the performance of the proposed methods.…”
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Alterations in regional shape on ipsilateral and contralateral cortex contrast in children with unilateral cerebral palsy and are predictive of multiple outcomes
Published 2016“…Using 139 structural magnetic resonance images, including 95 patients with clinically diagnosed CP and 44 TDC, cortical segmentations were obtained using a modified expectation maximization algorithm. Three shape characteristics (cortical thickness, curvature, and sulcal depth) were computed within a number of cortical regions. …”
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Collective interaction filtering approach for detection of group in diverse crowded scenes
Published 2019“…This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. …”
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Model-free viewpoint invariant human activity recognition
Published 2011“…Each pose model employs an expectation-maximization algorithm to estimate the probability that the given frame contains the corresponding pose. …”
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A monocular view-invariant fall detection system for the elderly in assisted home environments
Published 2011“…Each pose model employs an expectation-maximization algorithm to estimate the probability that the given frame contains the corresponding pose. …”
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Statistical modelling of time series of counts for a new class of mixture distributions / Khoo Wooi Chen
Published 2016“…Parameter estimation with the maximum likelihood estimation via the Expectation-Maximization algorithm is discussed and compared with the conditional least squares method. …”
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Characterisation of the central region of the sheep major histocompatibility complex
Published 2008“…Analysis of SNP panel genotypes in the cohort of 71 unrelated sheep using the expectation maximization algorithm permitted the prediction of a group of approximately 20 haplotypes, which accounted for more than 90% of the expected haplotype distribution. …”
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A simulation study of a parametric mixture model of three different distributions to analyze heterogeneous survival data
Published 2013“…In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data.Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated.The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters.The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme.The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model.The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors.…”
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A parametric mixture model of three different distributions: An approach to analyse heterogeneous survival data
Published 2014“…A parametric mixture model of three different distributions is proposed to analyse heterogeneous survival data.The maximum likelihood estimators of the postulated parametric mixture model are estimated by applying an Expectation Maximization Algorithm (EM) scheme.The simulations are performed by generating data, sampled from a population of three component parametric mixture of three different distributions. …”
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Mixture model of the Exponential, Gamma and Weibull distributions to analyse heterogeneous survival data
Published 2015“…Aims: In this study a survival mixture model of three components is considered to analyse survival data of heterogeneous nature.The survival mixture model is of the Exponential, Gamma and Weibull distributions.Methodology: The proposed model was investigated and the Maximum Likelihood (ML) estimators of the parameters of the model were evaluated by the application of the Expectation Maximization Algorithm (EM).Graphs, log likelihood (LL) and the Akaike Information Criterion (AIC) were used to compare the proposed model with the pure classical parametric survival models corresponding to each component using real survival data.The model was compared with the survival mixture models corresponding to each component.Results: The graphs, LL and AIC values showed that the proposed model fits the real data better than the pure classical survival models corresponding to each component.Also the proposed model fits the real data better than the survival mixture models corresponding to each component. …”
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