We develop an expectation-maximization algorithm to estimate the parameters of the Markov chain choice model. In this choice model, a customer arrives into the system to purchase a certain product. If this product is available for purchase, then the customer purchases it. Otherwise, the customer transitions between the products according to a transition probability matrix until she reaches an a...

We introduced the spectrum-adapted expectation-conditional maximization (ECM) algorithm to improve efficiency of peak fitting spectral data by various models. The ECM can perform using Pseudo–Voigt mixture model and Doniach–Šunjić–Gauss which are generally used for in X-ray photoelectron spectroscopy. Analyses synthetic experimental showed that proposed method quickly completed calculation esti...

In this paper we introduce a new algorithm for the estimation of source location parameters from array data given prior distributions on unknown nuisance source signal parameters. The conditional maximum-likelihood (CML) formulation is employed, and ML estimation is obtained by marginalizing over the nuisance parameters. In general, direct solution of this marginalization ML problem is intracta...

We consider a statistical model-based approach to the segmentation of magnetic resonance (MR) images with bias field correction. The proposed method of penalized maximum likelihood is implemented via the expectationconditional maximization (ECM) algorithm, using an approximation to the E-step based on a fractional weight version of the iterated conditional modes (ICM) algorithm. A Markov random...

We propose a new method for the Maximum Likelihood Estimator (MLE) of nonlinear mixed effects models when the variance matrix of Gaussian random effects has a prescribed pattern of zeros (PPZ). The method consists of coupling the recently developed Iterative Conditional Fitting (ICF) algorithm with the Expectation Maximization (EM) algorithm. It provides positive definite estimates for any samp...

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 injected noise speeds up the average convergence of the EM algorithm to a local maximum of the likelihood surface if a positivity condition holds. The gener...

Comparing a process of laborand capital-augmenting technical change directed by capitalists’ maximization of profits with a counterfactual in which decentralized innovation decisions are governed by noncapitalist property relations, I claim that if the two economies start from the same technology and capital stock there’s a date T such that after T per-capita consumption is always strictly grea...

The celebrated expectation-maximization (EM) algorithm is one of the most widely used optimization methods in statistics. In recent years it has been realized that EM algorithm is a special case of the more general minorization-maximization (MM) principle. Both algorithms creates a surrogate function in the first (E or M) step that is maximized in the second M step. This two step process always...

background: in this study, quantitative 32p bremsstrahlung planar and spect imaging and consequent dose assessment were carried out as a comprehensive phantom study to define an appropriate method for accurate dosimetry in clinical practice. materials and methods: ct, planar and spect bremsstrahlung images of jaszczak phantom containing a known activity of 32p were acquired. in addition, phanto...