Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 245 for “"Mixture Model"”.
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Bayesian Approach Dealing with Mixture Model Problems
… we focus on two research topics related to mixture models. The first topic is Adaptive Rejection Metropolis Simulated Annealing for Detecting Global Maximum Regions, and the second topic is Bayesian Model Selection for Nonlinear Mixed Effects Model. In the first topic, we consider a finite …
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Data Center Load Forecast Using Dependent Mixture Model
… work a stochastic method, based on dependent mixtures is developed to model the data center load and is used for day-ahead forecast. The method is validated using three data sets from National Renewable Energy Laboratory (NREL) and one other data centers. The proposed method proved better than …
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Degradation Analysis for Heterogeneous Data Using Mixture Model
… attention in recent years. Mixed-effects models have been frequently employed to analyze repeated-measures degradation data of multiple units. In existing studies, the test units are usually assumed to be sampled from a homogeneous population, and the random effects in the degradation …
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Bayesian Regression Inference Using a Normal Mixture Model
In this thesis we develop a two component mixture model to perform a Bayesian regression. We implement our model computationally using the Gibbs sampler algorithm and apply it to a dataset of differences in time measurement between two clocks. The dataset has ``good" time measurements and ``bad" …
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An improved Gaussian mixture model algorithm for background subtraction
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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Finite Mixture Model Specifications Accommodating Treatment Nonresponse in Experimental Research
… multiple signals from data streams with Gaussian mixture models, where their use is well matched to accommodate researchers in this predicament. While the mathematics underpinning models in either application remains unchanged, there are stark differences. In signal processing, results are …
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The Single Imputation Technique in the Gaussian Mixture Model Framework
… imputation technique in the basic regression model: the main motivation is that, the residual is added to improve the bias and variability. The residual is drawn by normal distribution assumption with a mean of 0, and the variance is equal to the residual variance. Although new methods in the …
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Analysis of Zero-Heavy Data Using a Mixture Model Approach
… has long been an interest in data analysis and modeling, however, there are no unique solutions to this problem. The solution to the individual problem really depends on its particular situation and the design of the experiment. For example, different biological, chemical, or physical processes …
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The Cauchy-Net Mixture Model for Clustering with Anomalous Data
… consequences, such as when building prediction models for housing prices. To combat anomalies, we develop the Cauchy-Net Mixture Model (CNMM). The CNMM is a flexible Bayesian nonparametric tool that employs a mixture between a Dirichlet Process Mixture Model (DPMM) and a Cauchy distributed …
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A Normal-Mixture Model with Random-Effects for RR-Interval Data
In many applications of random-effects models to longitudinal data, such as heart rate variability (HRV) data, a normal-mixture distribution seems to be more appropriate than the normal distribution assumption. While the random-effects methodology is well developed for several distributions in the …
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People Tracking Under Occlusion Using Gaussian Mixture Model and Fast Level Set Energy Minimization
… targets are represented with a Gaussian mixture, which are adapted to regions of the target automatically using an EM-model algorithm. Field speeds are defined for changed pixels in each frame based on the probability of their belonging to a particular person's blobs. Pixels are matched …
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Mixture Model Approaches to Integrative Analysis of Multi-Omics Data and Spatially Correlated Genomic Data
… behind a disease. Statistical methods can model the interrelationships of the involved gene activities through jointly analyzing multiple types of genomic data from different platforms (vertical integration), or improve the power of a study through aggregating the same type of genomic data …
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Weibull mixture model for grouped data and pattern identification in spatial and spatial-temporal data.
… and material science, we develop statistical models and apply statistical tools for characterizing patterns that exist in different types of data. In the first project, we propose the Weibull mixture model to fit the distribution of grain size in continental sediments in geological studies. We …
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Predicting Articular Cartilage Constituent Material Properties Following In Vitro Growth Using a Proteoglycan-Collagen Mixture Model
… electromechanical Poisson-Boltzmann unit cell model for proteoglycan interactions. The resulting proteoglycan model was combined with a novel collagen fibril model and a ground substance matrix material to create a polyconvex constitutive finite element model of a<a>rticular </a>cartilage. The …
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A New Breast Cancer Image Classifier Using Gaussian Mixture Model Based on Histogram and Enhanced Roughness Index
… In this thesis, a new method based on Gaussian Mixture Model (GMM) to perform the breast tumor classification into two different classes (benign class and malignant class) was proposed. Also a new Enhance Roughness Index (ERI) was developed. In the meanwhile, the two different factors (intensity …
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Assessing Robustness of the Rasch Mixture Model to Detect Differential Item Functioning - A Monte Carlo Simulation Study
… subgroups are likely to be neglected. The Rasch mixture model (RMM), a combination of the Rasch model and mixture model, is an alternative for extracting the latent class (LC) from summarizing similar identities of underlying latent traits. DIF can be calculated among LCs based on the differences …
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A new approach to calculate and forecast dynamic conditional correlation - the use of a multivariate heteroskedastic mixture model
… However, comparatively little is devoted to modelling time varying correlation. In this research, we extend the current literature on correlation modelling by reviewing existing time-series tools, performing empirical analysis and developing two new conditional heteroscedastic models based on …
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A study of computational methods to analyze gene expression data
… proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with …
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A machine learning approach to crystal structure prediction
… are extracted using probabilistic graphical models. Specific correlations are shown to reflect well-known structure stabilizing mechanisms. Two probabilistic models are investigated to represent correlation: an undirected graphical model known as a cumulant expansion, and a mixture model. The …
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Statistical methods to study heterogeneity of treatment effects
… this talk: a hypothesis testing procedure and a mixture-model based approach. The hypothesis testing procedure was constructed to test for the existence of a treatment effect in sub-populations. The test is nonparametric, and can be applied to all types of outcome measures. A key innovation of …
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