Results 61 to 70 of about 9,497,746 (229)
The Expectation-Maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood (ML) estimates, useful in a variety of incomplete-data problems.
Krishnan, Thriyambakam +2 more
core
Single-index logistic model for high-dimensional group testing data
AIMS MathematicsGroup testing is an efficient screening method that reduces the number of tests by pooling multiple samples, making it especially effective in low-prevalence settings. This strategy gained significant attention during the COVID-19 pandemic, and has since
Changfu Yang +4 more
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Gaussian Mixture Model Based Classification of Stuttering Dysfluencies
Journal of Intelligent Systems, 2016 The classification of dysfluencies is one of the important steps in objective measurement of stuttering disorder. In this work, the focus is on investigating the applicability of automatic speaker recognition (ASR) method for stuttering dysfluency ...
Mahesha P., Vinod D.S.
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Implementation of a fixing strategy and parallelization in a recent global optimization method [PDF]
, 2008 Electromagnetism-like Mechanism (EM) heuristic is a population-based stochastic global optimization method inspired by the attraction-repulsion mechanism of the electromagnetism theory.
Birbil, S. Ilker +3 more
core
On Convergence Properties of the EM Algorithm for Gaussian Mixtures
Neural Computation, 1996 We build up the mathematical connection between the Expectation-Maximization (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite gaussian mixtures.
L. Xu, Michael I. Jordan
semanticscholar +1 more source
Spectrum adapted expectation-maximization algorithm for high-throughput peak shift analysis
Science and Technology of Advanced Materials, 2019 We introduce a spectrum-adapted expectation-maximization (EM) algorithm for high-throughput analysis of a large number of spectral datasets by considering the weight of the intensity corresponding to the measurement energy steps.
Tarojiro Matsumura +4 more
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An Integrated Approach for Making Inference on the Number of Clusters in a Mixture Model
Entropy, 2019 This paper presents an integrated approach for the estimation of the parameters of a mixture model in the context of data clustering. The method is designed to estimate the unknown number of clusters from observed data.
Erlandson Ferreira Saraiva +3 more
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Achieving the oracle property of OEM with nonconvex penalties
Statistical Theory and Related Fields, 2017 Thepenalised least square estimator of non-convex penalties such as the smoothly clipped absolute deviation (SCAD) and the minimax concave penalty (MCP) is highly nonlinear and has many local optima.
Shifeng Xiong, Bin Dai, Peter Z. G. Qian
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پژوهشهای ریاضی, 2018 Estimation of statistical distribution parameter is one of the important subject of statistical inference. Due to the applications of Lomax distribution in business, economy, statistical science, queue theory, internet traffic modeling and so on, in this
Roshanak Zaman, Parviz Nasiri
doaj
Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements
IEEE AccessIn this paper, we have proposed a new paradigm for modeling of SAG mills. Typically, important parameters found in the modeling of such processes are described as state-space system model rather than unknown parameters.
Angel L. Cedeno +6 more
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