We investigate the application of the Bayesian expectation-maximization (BEM) technique to the design of soft-in soft-out (SISO) detection algorithms for wireless communication systems operating over channels affected by parametric uncertainty. First, the BEM algorithm is described in detail and its relationship with the well-known expectation-maximization (EM) technique is explained. Then, som...

Stable maximum likelihood estimation (MLE) of item parameters in 3PLM with a modest sample size remains a challenge. The current study presents a mixture-modeling approach to 3PLM based on which a feasible Expectation-Maximization-Maximization (EMM) MLE algorithm is proposed. The simulation study indicates that EMM is comparable to the Bayesian EM in terms of bias and RMSE. EMM also produces sm...

the aim of this study is to introduce a parametric mixture model to analysis the competing-risks data with two types of failure. in mixture context, i t h type of failure is i th component. the baseline failure time for the first and second types of failure are modeled as proportional hazard models according to weibull and gompertz distributions, respectively. the covariates affect on both the ...

| This paper presents a new algorithm, based on an EM (Expectation-Maximization) formulation, for ML (maximum likelihood) sequence estimation over unknown ISI (inter-symbol interference) channels with random channel coeecients which have a Gauss-Markov fast time-varying distribution. By using the EM formulation to marginalize over the channel coeecient distribution, maximum-likelihood estimates...

We present the Trusted Expectation-Maximization (TEM), a new discriminative training scheme, for speech recognition applications. In particular, the TEM algorithm may be used for Hidden Markov Models (HMMs) based discriminative training. The TEM algorithm has a form similar to the ExpectationMaximization (EM) algorithm, which is an efficient iterative procedure to perform maximum likelihood in ...

Image segmentation plays a major role in quantitative image analysis and computer aided detection (CAD) and diagnosis (CADx) for clinical applications. Conventional segmentation assigns a single label to each voxel, neglecting the partial volume (PV) effect. This work presents an EM (Expectation Maximization) framework for segmentation of tissue mixture in each voxel. Image data and tissue mixt...

Finite mixture models are being increasingly used in statistical inference and to provide a model-based approach to cluster analysis. Mixture models can be fitted to independent data in a straightforward manner via the expectation-maximization (EM) algorithm. In this paper, we look at ways of speeding up the fitting of normal mixture models by using variants of the EM, including the so-called s...

In this paper, we use a general mathematical and experimental methodology to analyze image deconvolution. The main procedure is to use an example image convolving it with a know Gaussian point spread function and then develop algorithms to recover the image. Observe the deconvolution process by adding Gaussian and Poisson noise at different signal to noise ratios. In addition, we will describe ...