The Expectation Maximization (EM) algorithm is a versatile tool for model parameter estimation in latent data models. When processing large data sets or data stream however, EM becomes intractable since it requires the whole data set to be available at each iteration of the algorithm. In this contribution, a new generic online EM algorithm for model parameter inference in general Hidden Markov ...

least squares method (lsm) is an accurate and rapid method for solving some analytical andnumerical problems. this method can be used to estimate the average reservoir pressure in well testanalysis. in fact, it may be employed to estimate parameters such as permeability (k) and porevolume (vp). regarding this point, buildup, drawdown, late transient test data, modified muskatmethod, interferenc...

We investigate novel algorithms to improve the convergence and reduce the complexity of time-domain convolutive blind source separation (BSS) algorithms. First, we propose MMax partial update time-domain convolutive BSS (MMax BSS) algorithm. We demonstrate that the partial update scheme applied in the MMax LMS algorithm for single channel can be extended to multichannel time-domain convolutive ...