Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is different and motivated by similar considerations to simulated annealing; that is we build a sequence of artificial distributions whose support concentrates itself on the set of maximum likelihood estimates. We sample...

In SPECT/PET, the maximum-likelihood expectation-maximization (ML-EM) algorithm is getting more attention as the speed of computers increases. This is because it can incorporate various physical aspects into the reconstruction process leading to a more accurate reconstruction than other analytical methods such as filtered-backprojection algorithms. However, the convergence rate of the ML-EM alg...

A novel method of reconstruction from single-photon emission computerized tomography data is proposed. This method builds on the expectation-maximization (EM) approach to maximum likelihood reconstruction from emission tomography data, but aims instead at maximum posterior probability estimation, which takes account of prior belief about smoothness in the isotope concentration. A novel modifica...

A (Stochastic) EM in General Expectation-Maximization (EM) is an iterative method for finding the maximum likelihood or maximum a posteriori (MAP) estimates of the parameters in statistical models when data is only partially, or when model depends on unobserved latent variables. This section is inspired from lecture of Dr Namrata Vaswani available at http://www.ece.iastate.edu/∼namrata/EE527 Sp...

In this paper, we present the Maximum Normalized Likelihood Estimation (MNLE) algorithm and its application for discriminative training of HMMs for continuous speech recognition. The objective of this algorithm is to maximize the normalized frame likelihood of training data. Instead of gradient descent techniques usually applied for objective function optimization in other discriminative algori...

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 application of Kalman filtering methods and maximum likelihood parameter estimation to models of commodity prices and futures prices has been considered by several authors. The usual method of finding the maximum likelihood parameter estimates (MLEs) is to numerically maximize the likelihood function. We present, as an alternative to numerical maximization of the likelihood, a filter-based ...

Expectation-maximization (EM) is an iterative algorithm for finding the maximum likelihood or maximum a posteriori estimate of the parameters of a statistical model with latent variables or when we have missing data. In this work, we view EM in a generalized surrogate optimization framework and analyze its convergence rate under commonly-used assumptions. We show a lower bound on the decrease i...

| 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 a sorting algorithm for the case of recurrent random comparison errors. The algorithm essentially achieves simultaneously good properties of previous algorithms for sorting n distinct elements in this model. In particular, it runs in O(n2) time, the maximum dislocation of the elements in the output is O(logn), while the total dislocation is O(n). These guarantees are the best possibl...