This note represents my attempt at explaining the EM algorithm (Hartley, 1958; Dempster et al., 1977; McLachlan and Krishnan, 1997). This is just a slight variation on TomMinka’s tutorial (Minka, 1998), perhaps a little easier (or perhaps not). It includes a graphical example to provide some intuition. 1 Intuitive Explanation of EM EM is an iterative optimizationmethod to estimate some unknown ...

Images of the inside of the human body can be obtained noninvasively using tomographic acquisition and processing techniques. In particular, these techniques are commonly used to obtain images of a -emitter distribution after its administration in the human body. The reconstructed images are obtained given a set of their projections, acquired using rotating gamma cameras. A general overview of ...

Using a theory of list-mode maximum likelihood expectation-maximization (MLEM) algorithm, in this contribution, we present a derivation of the system response kernel for a novel positron emission tomography (PET) detector based on plastic scintillators.

The expectation maximisation algorithm (EM) was introduced by Dempster, Laird and Rubin in 1977 [DLR77]. The basic of expextation maximisation is maximum likelihood estimation (MLE). In modern sensor data fusion expectation maximisation becomes a substantial part in several applications, e.g. multi target tracking with probabilistic multi hypothesis tracking (PMHT), target extraction within pro...

| We investigate multiuser signal detection with a base-station antenna array for synchronous CDMA Rayleigh fading uplink channels. We have developed a discrete-time model that enables the formulation of a spatial-temporal decorrelating detector using the maximum-likelihood criterion. The detector is shown to be near-far resistant. We further propose to implement the spatial-temporal decorrelat...

An algorithm for estimation of the parameters of a multiscale stochastic process based on scale-recursive dynamics on trees is presented. The expectation-maximization algorithm is used to provide maximum likelihood estimates for the general case of a nonhomogeneous tree with no fixed structure for the process dynamics. Experimental results are presented using synthetic data.

CONTENTS 1. Introduction 2. Image and blur models 3. Maximum likelihood (ML) parameter identification 3.1. Formulation 3.2. Constraints on the unknown parameters 4. ML parameter identification via the expectation-maximization (EM) algorithm 4.1. The EM algorithm in the linear Gaussian case 4.2. Choices of complete data 4.2.1. {x,y} as the complete data 4.2.2. {x,v} as the complete data Abstract...

The Expectation-Maximization (EM) algorithm is a very popular technique for maximum likelihood estimation in incomplete data models. When the expectation step cannot be performed in closed{form, a stochastic approximation of EM (SAEM) can be used. Under very general conditions, the authors have shown that the attractive stationary points of the SAEM algorithm correspond to the global and local ...