Topic
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.
Papers published on a yearly basis
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TL;DR: The I(2) model as discussed by the authors is defined as a submodel of the general vector autoregressive model, by two reduced rank conditions, and describes stochastic processes with stationary second difference.
Abstract: The I(2) model is defined as a submodel of the general vector autoregressive model, by two reduced rank conditions. The model describes stochastic processes with stationary second difference. A parametrization is suggested which makes likelihood inference feasible. Consistency of the maximum likelihood estimator is proved, and the asymptotic distribution of the maximum likelihood estimator is given. It is shown that the asymptotic distribution is either Gaussian, mixed Gaussian or, in some cases, even more complicated.
162 citations
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11 May 2003TL;DR: It is shown how maximum-likelihood estimation of those synchronization parameters can be implemented by means of the iterative expectation-maximization (EM) algorithm, and how the EM algorithm iterations can be combined with those of a turbo receiver, leading to a general theoretical framework for turbo synchronization.
Abstract: This paper is devoted to turbo synchronization, that is to say the use of soft information to estimate parameters like carrier phase, frequency offset or timing within a turbo receiver. It is shown how maximum-likelihood estimation of those synchronization parameters can be implemented by means of the iterative expectation-maximization (EM) algorithm [A.P. Dempster, et al., 1977]. Then we show that the EM algorithm iterations can be combined with those of a turbo receiver. This leads to a general theoretical framework for turbo synchronization. The soft decision-directed ad-hoc algorithm proposed in V. Lottici and M. Luise, [2002] for carrier phase recovery turns out to be a particular instance of this implementation. The proposed mathematical framework is illustrated by simulations reported for the particular case of carrier phase estimation combined with iterative demodulation and decoding [S. ten Brink, et al., 1998].
162 citations
01 Jan 2011
TL;DR: Maximum penalized likelihood estimation is proposed as a method for simultaneously estimating the background rate and the triggering density of Hawkes process intensities that vary over multiple time scales and used to examine self-excitation in Iraq IED event patterns.
Abstract: Estimating the conditional intensity of a self-exciting point process is particularly challenging when both exogenous and endogenous e!ects play a role in clustering. We propose maximum penalized likelihood estimation as a method for simultaneously estimating the background rate and the triggering density of Hawkes process intensities that vary over multiple time scales. We compare the accuracy of the algorithm with the recently introduced Model Independent Stochastic Declustering (MISD) algorithm and then use the model to examine self-excitation in Iraq IED event patterns.
161 citations
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TL;DR: In this paper, the authors examined the asymptotic properties of the maximum likelihood estimate and the model selection problem for independent observations coming from an unknown unknown distribution, and applied these results to model selection problems.
161 citations
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01 Dec 2000TL;DR: A new kernel rule has been developed for road sign classification using the Laplace probability density and an Expectation–Maximization algorithm is used to maximize the pseudo-likelihood function.
Abstract: Driver support systems (DSS) of intelligent vehicles will predict potentially dangerous situations in heavy traffic, help with navigation and vehicle guidance and interact with a human driver. Important information necessary for traffic situation understanding is presented by road signs. A new kernel rule has been developed for road sign classification using the Laplace probability density. Smoothing parameters of the Laplace kernel are optimized by the pseudo-likelihood cross-validation method. To maximize the pseudo-likelihood function, an Expectation–Maximization algorithm is used. The algorithm has been tested on a dataset with more than 4900 noisy images. A comparison to other classification methods is also given.
161 citations