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Outlier Detection via Parsimonious Mixtures of Contaminated Gaussian Distributions
Mixture models Model-based classification EM algorithm Contaminated Gaussian distribution Outlier detection Robust estimates Trimmed clustering
2013/6/14
For multivariate continuous data, the contaminated Gaussian distribution - having two parameters indicating the proportion of outliers and the degree of contamination - represents a convenient and nat...
In this short paper, we introduce a mixture of skew-t factor analyzers as well as a family of mixture models based thereon. The mixture of skew-t distributions model that we use arises as a limiting c...
Learning Mixtures of Bernoulli Templates by Two-Round EM with Performance Guarantee
Mixtures of Bernoulli Templates Two-Round EM Performance Guarantee
2013/6/13
Dasgupta showed that a two-round variant of the EM algorithm can learn mixture of Gaussian distributions with near optimal precision with high probability if the Gaussian distributions are well separa...
A spatio-spectral hybridization for edge preservation and noisy image restoration via local parametric mixtures and Lagrangian relaxation
Edge preserving smoother Semiparametric mixture model Partition of unity MISE Variational optimization Thin Plate Splines Spectral embedding Local template mod-els Multiple hypothesis testing.
2012/11/22
This paper investigates a fully unsupervised statistical method for edge preserving image restoration and compression using a spatial decomposition scheme. Smoothed maximum likelihood is used for loca...
Mixtures of Shifted Asymmetric Laplace Distributions
Mixtures Shifted Asymmetric Laplace Distributions
2012/9/19
A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classi cation. A variant of the EM algorithm is developed for parameter estimation by exploiting the rel...
Maximum Likelihood Estimation of Gaussian Cluster Weighted Models and Relationships with Mixtures of Regression
Cluster-weighted modeling finite mixtures of regression EM-algorithm
2012/9/19
Cluster-weighted modeling (CWM) is a mixture approach for modeling the joint probability of a response variable and a set of explanatory variables. The parame-ters are estimated by means of the expect...
On the canonical form of scale mixtures of skew-normal distributions
affine invariance kurtosis Mardia indices of multivariate skewness and kurtosis scale mixtures of normal distributions skewness skew-normal distribution skewtdistribution.
2012/9/18
The canonical form of scale mixtures of multivariate skew-normal distribution is defined,emphasizing its role in summarizing some key properties of this class of distributions. It is also shown that t...
Decision Based Uncertainty Propagation Using Adaptive Gaussian Mixtures
Adaptive Gaussian Sum Decision Making
2011/7/19
Given a decision process based on the approximate probability density function returned by a data assimilation algorithm, an interaction level between the decision making level and the data assimilati...
Convergence rate for predictive recursion estimation of finite mixtures
Density estimation Kullback–Leibler divergence
2011/7/6
Predictive recursion (PR) is a fast stochastic algorithm for nonparametric estimation of mixing distributions in mixture models.
On Normal Variance-Mean Mixtures
convex contours distribution theory generalized inverse Gaussian distribution
2011/7/5
Normal variance-mean mixtures encompass a large family of useful distributions such as the generalized hyperbolic distribution, which itself includes the Student t, Laplace, hyperbolic, normal inverse...
A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures
metabolomics concentration estimation prior information multi component model block updates
2011/6/17
Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to
obtain profiles of metabolites dissolved in biofluids such as cell supernatants. Methods
for estimating metabolite concent...
Some covariance models based on normal scale mixtures
cross covariance function Gneiting's class rainfall model spatio-temporal model
2011/3/24
Modelling spatio-temporal processes has become an important issue in current research. Since Gaussian processes are essentially determined by their second order structure, broad classes of covariance ...
Perfect Simulation for Mixtures with Known and Unknown Number of components
Bounding chains Dirichlet process Gibbs sampling Mixtures Optimization Perfect Sam-pling
2011/3/24
We propose and develop a novel and effective perfect sampling methodology for simulating from posteriors corresponding to mixtures with either known (fixed) or unknown number of components. For the la...
Perfect Simulation for Mixtures with Known and Unknown Number of components
Bounding chains Dirichlet process Gibbs sampling Mixtures Optimization Perfect Sam-pling
2011/3/23
We propose and develop a novel and effective perfect sampling methodology for simulating from posteriors corresponding to mixtures with either known (fixed) or unknown number of components. For the la...
Stochastic model selection for Mixtures of Matrix-Normals
Mixture models birth and death process Gibbs sampler
2010/10/19
Finite mixtures of matrix normal distributions are a powerful tool for classifying three-way data in unsupervised problems. The distribution of each component is assumed to be a matrix variate normal ...