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About 10,000 papers for “Mixture model”
Paul M. Bays (685447), Ben A. Dowding (3804361) · Figshare · 2017
<p>The model of value estimates consisted of a mixture of a beta distribution (blue), corresponding to imprecise recall of the target value, and a normal distribution (green), capturing guessing.</p>
Cited by 0Open access
Jeroen Atsma (3180744), Femke Maij (3180747), Mathieu Koppen (837255) et al. · Figshare · 2016
<p>The presaccadic location of the NT (square) is reported after a transsaccadic displacement of 10° to the right. Objects in red represent visible targets; the white objects depict the veridical target locations. Representations of location estimates, modeled as 2D Gaussians, are shown as dark ellipses. (A) Before the saccade, all three objects are encoded with the foveal prior <i>f</i> (light grey blob) being centered at the triangle, the FT. After the saccade, the displaced target’s position and identity (NT here) are encoded with <i>f</i> now being centered at the saccade landing position. (B) Based on the NT’s presaccadic (<i>m</i>) and postsaccadic (<i>v</i>) representations, both biased by <i>f</i>, the probability of a single stable object, p(C|mv), is computed. In case <i>m</i> and <i>v</i> are unrelated the best solution is to segregate and ignore <i>v</i>. If <i>m</i> and <i>v</i> derive from the same object, the best solution is to integration all signals. (D) The two solutions in (C) are weighted according to the probability that <i>m</i> and v are related. The localization response follows from .</p>
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Rohan A. Baxter · Encyclopedia of Machine Learning and Data Mining · 2016
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Vermunt, J.K., Lewis-Beck, M.S., Bryman, A. et al. · Data Archiving and Networked Services (DANS) · 2004
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DESSEIN, Arnaud · TIB Data Manager · 2024
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2014
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Troy Neilson (19556704), Joshua Byrnes (835387), Nicholas Rohde (3314919) · Figshare · 2024
<p>Table 3 captures the results of the three component model outlined in the methodology, where a multinomial logit was used to predict class membership based on Total Deal Value. The finite mixture model demonstrates a downward trend in total deal value from class 1. This effect is most prominent in class 2 with a coefficient of -8.6953 on total deal value, -6.2622 for total deal value in class 3.</p>
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Sam Mavandadi (165707), Steve Feng (165718), Frank Yu (165723) et al. · Figshare · 2015
<p>Each observation made by the expert can take one of three category values from . The parameters governing the model are and , where is the true category of the observed data point, and and correspond to the expert labelling the data point as or , respectively.</p>
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Lowengrub Steven, M. Wise, V. Cristini et al. · 2007
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Claude Sammut, Geoffrey I. Webb · Encyclopedia of Machine Learning · 2010
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Filomena Maggino · 2023
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Pedro Larrañaga, Concha Bielza · Dictionary of Bioinformatics and Computational Biology · 2004
Cited by 20
Javier Cruz-Salgado · Ingeniería Investigación y Tecnología · 2016
Ingeniería Investigación y Tecnología es una publicación trimestral, arbitrada, de investigación científica y tecnológica editada e impresa por la Facultad de Ingeniería de la UNAM. Su propósito es difundir información y conocimientos originales e inéditos relacionados con la ingeniería. Es un espacio para profesores, investigadores, profesionales y estudiantes de todo el mundo.
Cited by 7Open access
Lijie Yu · 2015
In recent years, rapid growth in wind energy as a substantial source of electricity generation has created greater demands on wind turbine system reliability and availability. To reduce service costs and maximize return on investment, wind farm operators have begun to take a more proactive approach to turbine problems by relying on intelligent condition monitoring and automated failure detection systems. The challenge is how to effectively convert large amounts of data into actionable decisions to detect and isolate failures at an early stage. This paper describes a unique data analysis and modeling technique for online turbine health monitoring and automatic root cause assessment. It provides a means to capture failure signatures for specific root causes based on historical events as well as engineering knowledge. Both continuous and discrete turbine condition monitoring data are processed to provide a failure probability assessment. First, statistical trend analysis, feature extraction and classification methods are developed to analyze a continuous sensor data set. Secondly, a pattern recognition method is applied to calculate failure indicators from various discrete control system events, or fault messages. Then failure likelihoods derived from both the continuous and the discrete models are combined in a fusion model to increase predictive accuracy. A demonstration of the method on bearing failure modeling using SCADA data will be provided with promising results.
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Vincent Garcia, Frank Nielsen, Richard Nock · 2010
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually com- plex and further needs to be simplified. In this paper, we present a GMM simplification method based on a hierarchical clustering algo- rithm. Our method allows one to first to quickly compute a compact version of the initial GMM, and second to automatically learn the optimal number of components of the simplified GMM. Using the framework of Bregman divergences, this simplification algorithm, although presented here for GMMs, is suitable for any mixture of exponential families.
Cited by 22
John A. Cornell · Wiley series in probability and statistics · 2011
This chapter contains sections titled: The Inclusion of Inverse Terms in the Scheffé Polynomials Fitting Gasoline Octane Numbers Using Inverse Terms in the Model An Alternative Model Form for Modeling the Additive Blending Effect of One Component In a Multicomponent System A Biological Example on the Linear Effect of a Powder Pesticide In Combination With Two Liquid Pesticides Used for Suppressing Mite Population Numbers The Use of Ratios of Components Cox's Mixture Polynomials: Measuring Component Effects An Example Illustrating the Fits of Cox's Model and Scheffé's Polynomial Fitting A Slack-Variable Model A Numerical Example Illustrating The Fits of Different Reduced Slack-Variable Models: Tint Strength of a House Paint Summary References and Recommended Reading Questions
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Fabien Mavrot (3477245), Karin Orsel (763534), Wendy Hutchins (3147561) et al. · Figshare · 2020
<p>Mixture distribution models for ELISA results of serum and filter paper samples from free-ranging muskoxen tested against <i>Erysipelothrix rhusiopathiae</i>. Black curve: frequency distribution of the ELISA results (Percent Positivity) for the datasets (serum or filter paper samples). Blue and red curves: estimated underlying distributions of negative and positive samples, respectively. Dotted vertical lines: cut-off value obtained through mixture distribution analysis. Grey area: 95% Confidence Intervals (CI) computed through 1,000 bootstrap iterations. Optimal cut-offs were estimated at 0.25 (95% CI = 0.23–0.28) and 0.48 (95% CI = 0.35–0.59) for serum and filter paper samples, respectively.</p>
Cited by 0Open access
Daniel McNeish · OSF Preprints (OSF Preprints) · 2020
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G. Ueno, Seiji Zenitani · Physics of Plasmas · 2021
For analyzing distribution functions of relativistic plasma, we propose a mixture model composed of relativistic Maxwellian distributions. We first summarize the basic properties of the relativistic Maxwellian distribution, including the derivation of the normalization constant when there is a bulk velocity. We also examine the maximum likelihood estimation of the relativistic Maxwellian distribution. We then introduce a relativistic Maxwellian mixture model (R-MMM), which is a weighted sum of relativistic Maxwellian distributions. We develop an expectation–maximization algorithm for estimating the parameters of R-MMM, namely, the mixing proportion, the bulk velocity, and the temperature of each component. We apply a two-component R-MMM to a distribution function by a particle-in-cell (PIC) simulation of relativistic pair plasma and separate the simulated distribution function into two components. We find that one component has a large bulk velocity while the other is almost stagnant, and that the two components have almost the same temperatures, which is also consistent with the initial temperature of PIC simulation.
Cited by 1Open access
John Edward, Radcliffe · Cronfa (Swansea University) · 2005
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