In statistics, an expectationmaximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated usin...

Many researchers believe that consumers’ decisions are not only decided by their personal tastes, but also by the decisions of people who are in their networks. On the other hand, social scientists are more interested in consumers’ dichotomous choice. So an auto-probit model accommodating multiple networks are very useful. However, Current methods to investigate multiple autocorrelated network ...

Foetal weight estimation is a clinically relevant task for proper medical care in perinatal situations. Usually this estimation is based on features such as measurements derived from echographic examinations. Several formulas have been developed by other authors for performing this estimation with limited degree of success. Our approach is based on multilayer perceptrons (MLP) and radial basis ...

In two-way OFDM relay, carrier frequency offsets (CFOs) between relay and terminal nodes introduce severe intercarrier interference (ICI) which degrades the performance of traditional physical-layer network coding (PLNC). Moreover, traditional algorithm to compute the posteriori probability in the presence of ICI would incur prohibitive computational complexity at the relay node. In this paper,...

We design a language model based on a generative dependency structure for sentences. The parameter of the model is the probability of a dependency N-gram, which is composed of lexical words with four kinds of extra tags used to model the dependency relation and valence. We further propose an unsupervised expectationmaximization algorithm for parameter estimation, in which all possible dependenc...

Hidden Markov models have successfully been applied as models of discrete time series in many fields. Often, when applied in practice, the parameters of these models have to be estimated. The currently predominating identification methods, such as maximumlikelihood estimation and especially expectation-maximization, are iterative and prone to have problems with local minima. A non-iterative met...

Object detection when provided image-level labels instead of instance-level labels (i.e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain. In this paper, we address this challenging problem by developing an ExpectationMaximization (EM) based object detection method using deep c...

A recent trend in motion-based segmentation has been to rely on statistical procedures derived from ExpectationMaximization (EM) principles. EM-based approaches have various attractives for segmentation, such as proceeding by taking non-greedy soft decisions with regards to the assignment of pixels to regions, or allowing the use of sophisticated priors capable of imposing spatial coherence on ...

Corresponding author Abstract Data with missing sample-values are quite common in many microarray expression profiles. The outcome of the analysis of these microarray data mostly depends on the quality of underlying data. In fact, without complete data, most computational approaches fail to deliver the expected performance. So, filling out missing values in the microarray, if any, is a prerequi...

Segmentation of CSF and pulsative blood flow, based on a single phase contrast MRA (PC-MRA) image can lead to imperfect classifications. In this paper, we present a novel automated flow segmentation method by using PC-MRA image series. The intensity time series of each pixel is modeled as an autoregressive (AR) process and features including the Linear Prediction Coefficients (LPC), covariance ...