搜索结果: 1-15 共查到“理论统计学 the mixture”相关记录28条 . 查询时间(0.089 秒)
Estimating Mixture of Gaussian Processes by Kernel Smoothing
Identifiability EM algorithm Kernel regression Gaussian process Functional principal component analysis
2016/1/20
When the functional data are not homogeneous, e.g., there exist multiple classes of func-tional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimati...
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
Dynamic Clustering Asymptotics Dependent Dirichlet Process Mixture
2013/6/17
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The alg...
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering
Quantum annealing Dirichlet process Stochastic optimization Maximum a posteriori estimation Bayesian nonparametrics
2013/6/17
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA)...
Adaptive Metropolis-Hastings Sampling using Reversible Dependent Mixture Proposals
Ergodic convergence Markov Chain Monte Carlo Metropolis-within Gibbs composite sampling Multivariatet mixtures Simulated annealing Variational Approx-imation
2013/6/14
This article develops a general-purpose adaptive sampler that approximates the target density by a mixture of multivariate t densities. The adaptive sampler is based on reversible proposal distributio...
A Mixture of Generalized Hyperbolic Distributions
Mixture Generalized Hyperbolic Distributions
2013/6/13
We introduce a mixture of generalized hyperbolic distributions as an alternative to the ubiquitous mixture of Gaussian distributions as well as their near relatives of which the mixture of multivariat...
PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes
Profile regression Clustering Dirichlet process mixture model
2013/4/27
PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vect...
Finite mixture models with predictive recursion marginal likelihood
Density estimation Dirichlet distribution mixture com-plexity
2011/7/6
Estimation of finite mixture models when the mixing distribution support is unknown is an important and challenging problem. In this paper, a new approach is given based on the recently proposed predi...
Hidden Markov Mixture Autoregressive Models: Parameter Estimation
Hidden Markov Model Mixture Autoregressive Model Parameter Estimation
2011/6/17
This report introduces a parsimonious structure for mixture of au-
toregressive models, where the weighting coefficients are determined
through latent random variables as functions of all past obser...
Hidden Markov Mixture Autoregressive Models: Stability and Moments
Hidden Markov Model Mixture Autoregressive Model Stability Dynamic Programming Forecasting
2011/6/16
This paper introduces a new parsimonious structure for mixture
of autoregressive models. The weighting coefficients are determined
through latent random variables, following a hidden Markov model.
...
We propose a general modeling framework for marked Poisson processes observed over time or space. The modeling approach exploits the connection of the nonhomogeneous Poisson process intensity with a d...
A factor mixture analysis model for multivariate binary data
model based clustering latent trait analysis EM algorithm
2010/10/19
The paper proposes a latent variable model for binary data coming from an unobserved heterogeneous population. The heterogeneity is taken into account by replacing the traditional assumption of Gauss...
Free energy methods for efficient exploration of mixture posterior densities
Adaptive Biasing Force Adaptive Biasing Potential Adaptive Markov chainMonte Carlo Importance sampling Mixture models
2010/3/11
Because of their multimodality, mixture posterior densities are difficult to sample with
standard Markov chain Monte Carlo (MCMC) methods. We propose a strategy to enhance
the sampling of MCMC in th...
Improved EM for Mixture Proportions with Applications to Nonparametric ML Estimation for Censored Data
AECM cocktail algorithm data augmentation doubly censored data EM globalconvergence NPMLE nonparametric mixtures
2010/3/10
Improved EM strategies, based on the idea of efficient data augmentation (Meng and van
Dyk 1997, 1998), are presented for ML estimation of mixture proportions. The resulting
algorithms inherit the s...
Correction to:“Blind maximum likelihood separation of a linear-quadratic mixture”
Correction Blind maximum likelihood separation linear-quadratic mixture
2010/3/9
An error occurred in the computation of a gradient in [1]. The equa-
tions (20) in Appendix and (17) in the text were not correct. The current
paper presents the correct version of these equations.
Spatial Mixture Modelling for Unobserved Point Processes: Examples in Immunofluorescence Histology
Bayesian computation blocked Gibbs sampler Dirichlet process mixture model inhomogeneous Poisson process
2009/9/24
We discuss Bayesian modelling and computational methods in analysis of indirectly observed spatial point processes. The context involves noisy measurements on an underlying point proces...