We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the convergence of the EM algorithm. The NEM theorem shows that additive noise speeds up the average convergence of the EM algorithm to a local maximum of the likelihood surface if a positivity condition holds. Corollary...

In the previous class we already mentioned that many of the most powerful probabilistic models contain hidden variables. We will denote these variables with y. It is usually also the case that these models are most easily written in terms of their joint density, p(d,y,θ) = p(d|y,θ) p(y|θ) p(θ) (1) Remember also that the objective function we want to maximize is the log-likelihood (possibly incl...

The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used ...

Iterative maximum-likelihood expectation maximization and ordered-subset expectation maximization algorithms are excellent for image reconstruction and usually provide better images than filtered backprojection (FBP). Recently, an FBP algorithm able to incorporate noise weighting during reconstruction was developed. This paper compares the performance of the noise-weighted FBP algorithm and the...

Data acquired with the charge coupled device camera on the HST are modeled as an additive Poisson-Gaussian mixture, with the Poisson component representing cumulative counts of object-dependent photoelectrons, object-independent photoelectrons, bias electrons and thermoelectrons, and the Gaussian component representing read-out noise. Two methods are examined for compensating for read-out noise...

In this paper, we study the autoregressive (AR) model with normal inverse Gaussian (NIG) innovations. The NIG distribution is semi heavy-tailed and helpful in capturing extreme observations present data. expectation-maximization (EM) algorithm used to estimate parameters of considered AR(p) model. efficacy estimation procedure shown on simulated data for AR(2) AR(1) models. A comparative presen...

To unravel the driving patterns of networks, most popular models rely on community detection algorithms. However, these approaches are generally unable to reproduce structural features network. Therefore, attempts always made develop that incorporate network properties beside structure. In this work, we present a probabilistic generative model and an efficient algorithm both perform capture rec...