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An Improved EM algorithm
Sensitivity analysis Convergence analysis Expectation Maximization K-means K-medoids
2013/6/14
In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we g...
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...
An EM Algorithm for Continuous-time Bivariate Markov Chains
Parameter estimation EM algorithm Continuous-time bivariate Markov chain
2011/7/19
We study properties and parameter estimation of finite-state homogeneous continuous-time bivariate Markov chains.
Sequential Monte Carlo EM for multivariate probit models
Maximum likelihood Multivariate probit Monte Carlo EM adaptive sequential Monte Carlo
2011/7/19
A Monte Carlo EM algorithm is considered for the maximum likelihood estimation of multivariate probit models.
Closed-Form EM for Sparse Coding and Its Application to Source Separation
Closed-Form EM Sparse Coding Source Separation
2011/6/17
We define and discuss the first sparse coding algorithm based on closed-form EM
updates and continuous latent variables. The underlying generativemodel consists
of a flexibly parameterized ‘spike-an...
The expectation-maximization (EM) algorithm introduced by Dempster et al [12] in 1977 is a
very general method to solve maximum likelihood estimation problems. In this informal report,
we review the...
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...
EM versus Markov chain Monte Carlo for estimation of hidden Markov models: a computational perspective
hidden Markov model incomplete data missing data EM trans-dimensional Monte Carlo computational statistics
2009/9/22
Hidden Markov models (HMMs) and related models have become stan-
dard in statistics during the last 15C2 years, with applications in diverse areas
like speech and other statistical signal processing...
PERFORMANCE OF THE EM ALGORITHM ON THE IDENTIFICATION OF A MIXTURE OF WATSON DISTRIBUTIONS DEFINED ON THE HYPERSPHERE
EM algorithm mixture principal components Watson distribution
2009/2/25
We consider a set of n individuals described by p standardised variables, and we sup-pose that the individuals are previously selected from a population and the variables are a sample of variables ass...
Active Set and EM Algorithms for Log-Concave Densities Based on Complete and Censored Data
Active Set EM Algorithms Log-Concave Densities Complete and Censored Data
2010/4/30
We develop an active set algorithm for the maximum likelihood estimation of a
log–concave density based on complete data. Building on this fast algorithm, we introduce an EM algorithm to treat arbitr...