We develop a new algorithm for the tracking of radioactive particles using Positron Emission Particle Tracking (PEPT). The relies on maximization likelihood simple Gaussian mixture model lines response associated with positron annihilation. includes component that accounts spurious caused by scattering and random coincidence, it treats relative activity as well their positions parameters to be ...

The expectation maximization (EM) algorithm computes maximum likelihood estimates of unknown parameters in probabilistic models involving latent variables. More pragmatically speaking, the EM algorithm is an iterative method that alternates between computing a conditional expectation and solving a maximization problem, hence the name expectation maximization. We will in this work derive the EM ...

The paper gives a brief review of the expectation-maximization algorithm (Dempster, Laird, and Rubin 1977) in the comprehensible framework of discrete mathematics. In Section 2, two prominent estimation methods, the relative-frequency estimation and the maximum-likelihood estimation are presented. Section 3 is dedicated to the expectation-maximization algorithm and a simpler variant, the genera...

This paper considers parameter estimations in Lomax distribution under progressive type-II censoring with random removals, assuming that the number of units removed at each failure time has a binomial distribution. The maximum likelihood estimators (MLEs) are derived using the expectation-maximization (EM) algorithm. The Bayes estimates of the parameters are obtained using both the squared erro...

We develop an expectation-maximization algorithm with local adaptivity for image segmentation and classification. The key idea of our approach is to combine global statistics extracted from the Gaussian mixture model or other proper statistical models with local statistics and geometrical information, such as local probability distribution, orientation, and anisotropy. The combined information ...

In three-dimensional (3-D) fluorescence microscopy, a series of two-dimensional (2-D) images is collected at different focal settings through the specimen. Each image in this series contains the in-focus plane plus contributions from out-of-focus structures that blur the image. Furthermore, as the series is collected the fluorescent dye in the specimen fades over time in response to the total e...

The ‘expectation–conditional maximization either’ (ECME) algorithm has proven to be an effective way of accelerating the expectation–maximization algorithm for many problems. Recognizing the limitation of using prefixed acceleration subspaces in the ECME algorithm, we propose a dynamic ECME (DECME) algorithm which allows the acceleration subspaces to be chosen dynamically. The simplest DECME im...

This paper proposes an algorithm which can write programs automatically to solve problems. We model the sequence of instructions as a n-gram language model and the sequence is represented by some hidden variables. Expectation maximization algorithm is applied to train the n-gram model and perform program induction. Our approach is very flexible and can be applied to many problems. In this paper...

Clustering is an intensive research for some years because of its multifaceted applications, such as biology, information retrieval, medicine, business and so on. The expectation maximization (EM) is a kind of algorithm framework in clustering methods, one of the ten algorithms of machine learning. Traditionally, optimization of objective function has been the standard approach in EM. Hence, re...