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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...
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...
We study properties and parameter estimation of finite-state homogeneous continuous-time bivariate Markov chains.
A Monte Carlo EM algorithm is considered for the maximum likelihood estimation of multivariate probit models.
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 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...
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...
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...
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...

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