Expectation–maximization algorithm

About: Expectation–maximization algorithm is a research topic. Over the lifetime, 11823 publications have been published within this topic receiving 528693 citations. The topic is also known as: EM algorithm & Expectation Maximization.

A new probabilistic generative model of parameter inference in biochemical networks

Abstract: We present a new method for estimating rate coefficients and level of noise in models of biochemical networks from noisy observations of concentration levels at discrete time points. Its probabilistic formulation, based on maximum likelihood estimation, is key to a principled handling of the noise inherent in biological data, and it allows for a number of further extensions, such as a fully Bayesian treatment of the parameter inference and automated model selection strategies based on the comparison between marginal likelihoods of different models. We developed KInfer (Knowlegde Inference), a tool implementing our inference model. KInfer is downloadable for free at http://www.cosbi.eu.

The Use of Restricted Latent Class Models for Defining and Testing Nonparametric and Parametric Item Response Theory Models.

Abstract: A general class of ordinal logit models is presented that specifies equality and inequality constraints on sums of conditional response probabilities. Using these constraints in latent class analysis, models are obtained that are similar to parametric and nonparametric item response models. Maximum likelihood is used to estimate these models, making their assumptions testable with likelihood-ratio statistics. Because of the intractability of the asymptotic distribution of the goodness-of-fit measure when imposing inequality constraints, parametric bootstrapping is used to obtain estimates of p values. The proposed restricted latent class models are illustrated by an example using reported adult crying behavior.

Data-Driven Imputation Method for Traffic Data in Sectional Units of Road Links

Abstract: Missing data imputation is a critical step in data processing for intelligent transportation systems. This paper proposes a data-driven imputation method for sections of road based on their spatial and temporal correlation using a modified $k$ - nearest neighbor method. This computing-distributable imputation method is different from the conventional algorithms in the fact that it attempts to impute missing data of a section with multiple sensors that have correlation to each other, at once. This increases computational efficiency greatly compared with other methods, whose imputation subject is individual sensors. In addition, the geometrical property of each section is conserved; in other words, the continuation of traffic properties that each sensor captures is conserved, therefore increasing accuracy of imputation. This paper shows results and analysis of comparison of the proposed method to others such as nearest historical data and expectation maximization by varying missing data type, missing ratio, traffic state, and day type. The results show that the proposed algorithm achieves better performance in almost all of the missing types, missing ratios, day types, and traffic states. When the missing data type cannot be identified or various missing types are mixed, the proposed algorithm shows accurate and stable imputation performance.

Fitting mixtures of linear regressions

Abstract: In most applications, the parameters of a mixture of linear regression models are estimated by maximum likelihood using the expectation maximization (EM) algorithm. In this article, we propose the comparison of three algorithms to compute maximum likelihood estimates of the parameters of these models: the EM algorithm, the classification EM algorithm and the stochastic EM algorithm. The comparison of the three procedures was done through a simulation study of the performance (computational effort, statistical properties of estimators and goodness of fit) of these approaches on simulated data sets. Simulation results show that the choice of the approach depends essentially on the configuration of the true regression lines and the initialization of the algorithms.