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Showing 1 to 20 of 4740 for “"mixture"”.
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Semiparametric mixture models
… three parts that are related to semiparametric mixture models. In Part I, we construct the minimum profile Hellinger distance (MPHD) estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other component …
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Prediction with Mixture Models
… to mirror this structure in the model. Such mixture models predict by combining the individual predictions generated by the mixture components which correspond to the partitions in the data. Often the partitioned structure is latent, and has to be inferred when learning the mixture model. …
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Algorithms for Mixture Models
Mixture models form one of the most fundamental classes of generative models for clustered data. Specific application examples include text classification problems, image segmentation and motion detection, collaborative filtering and many others. However, quite surprisingly, very little had been …
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A response surface approach to the mixture problem when the mixture components are categorized
A method is developed for experiments with mixtures where the mixture components are categorized (acids, bases, etc.), and each category of components contributes a fixed proportion to the total mixture. The number of categories of mixture components is general and each category will be represented …
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Mixture Modeling for Multivariate Observations
… especially for multivariate observations. Mixtures, particularly nonparametric and semiparametric mixtures, have the potential to outperform the kernel-based methods, as has been shown in previous research in the univariate case. It is the main goal of this research to extend the …
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Least squares mixture decomposition estimation
The Least Squares Mixture Decomposition Estimator (LSMDE) is a new nonparametric density estimation technique developed by modifying the ordinary kernel density estimators. While the ordinary kernel density estimator assumes equal weight (l/<i>n</i>) for each data point, LSMDE assigns the optimized …
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Efficient algorithms for learning mixture models
… for a class of probabilistic models called mixture models. Mixture models are usually used to model settings where the observed data consists of different sub-populations, yet we only have access to a limited number of samples of the pooled data. It includes many widely used models such as …
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Robust mixture regression using mean-shift penalisation
The purpose of finite mixture regression (FMR) is to model the relationship between a response and feature variables in the presence of latent groups in the population. The different regression structures are quantified by the unique parameters of each latent group. The Gaussian mixture regression …
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Supervised Classification Using Copula and Mixture Copula
… In fact for some data, the pattern vector is a mixture of discrete and continuous random variables. In this dissertation, we use copula densities to model class conditional distributions. Such types of densities are useful when the marginal densities of a pattern vector are not normally …
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On neural spike sorting with mixture models
… attempts to develop a new set of statistical mixture models and methods and apply them to the neural data analysis. The problem we are trying to solve is called neural spike sorting in literature. There are three basic objectives of spike sorting. The first is to estimate the number of neurons …
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Column separation and waterhammer in binary mixture
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1983.
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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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Application of the EM Algorithm for Mixture Models
… (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as well as two other EM-based algorithms for speeding up …
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Molecular Code Division Multiple Access: Gaussian Mixture Modeling
… molecular signal is modeled as a Gaussian mixture distribution when the MC system undergoes Brownian noise and inter-symbol interference (ISI). This novel approach demonstrates a suitable modeling for diffusion-based MC system. Using the proposed Gaussian mixture model, a simple receiver is …
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Advances in mixture modeling and model based clustering
… model-based which relies on the idea of finite mixture models. This dissertations will propose new advances in clustering area mostly related to model-based clustering and its extension to the K-means algorithm. This report has five chapters. The first chapter is a literature review on recent …
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Using Dirichlet Process Priors For Bayesian Mixture Clustering
We describe a non-parametric Bayesian model using genotype data to classify individuals among populations where the total number of populations is unknown. The model assumes that a population is characterized by a set of allele frequencies that follow multinomial distributions. The Dirichlet …
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Explosive property of aluminum powder liquid oxygen mixture
… the optimum conditions for explosion of a mixture of aluminum powder and liquid oxygen when fired without the use of a fixed detonator.</p>
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A finite mixture approach for household residential choices
… typing of households by implementing a finite mixture model which gives a probability distribution of an individual household being a particular type. The model best fitted the array of households into two types. The types exhibited differences in their attitudinal and demographic …
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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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