The stochastic EM algorithm: Estimation and asymptotic results.

Topics include numerical optimization in statistical inference including expectation-maximization (EM) algorithm, Fisher scoring, gradient descent and stochastic gradient descent, etc., numerical integration approaches include basic numerical quadrature and Monte Carlo methods, and approximate Bayesian inference methods including Markov chain Monte Carlo, variational inference and their.

Numerical experiments based on simulated and real data illustrate the performance of the proposed methods. For fitting nonlinear mixed effects models, the suggested MH algorithm is efficiently combined with a stochastic approximation version of the EM algorithm for maximum likelihood estimation of the global parameters. Agid: 6543923.

Stochastic image models for algorithm design. Daniel Robert Tretter, Purdue University. Abstract. In this work two different stochastic image models are proposed for use in two different areas of image processing. First, we develop both a theory and specific methods for performing optimal transform coding of multispectral and multilayer images.

These applications range from stochastic optimization methods and algorithms, to online forms of the EM algorithm, reinforcement learning via temporal differences, and deep learning, and others. Stochastic approximation algorithms have also been used in the social sciences to describe collective dynamics: fictitious play in learning theory and consensus algorithms can be studied using their.

Hence, a generalization of the EM algorithm to semiparametric mixture models is proposed. The approach is methodological and can be applied to a wide class of semiparametric mixture models. The behavior of the proposed EM type estimators is studied numerically not only through several Monte-Carlo experiments but also through comparison with alternative methods existing in the literature.

Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training Tin Y Lam 1 and Irmtraud M Meyer 1 1 Centre for High-Throughput Biology, Department of Computer Science and Department of Medical Genetics, 2366 Main Mall, University of British Columbia, Vancouver V6T 1Z4, Canada.

Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints. Stochastic optimization methods also include methods with random iterates.

If we want to implement a Logistic Regression Algorithm with Stochastic Gradient Descent (SDG), which of the following loss functions could be used for updating the ith training data?

I am reading the paper: Convergence of a stochastic approximation version of the EM algorithm to implement this algorithm for a probability model I already have. In p. 3, the paper summarises the algorithm as follows. I am stuck at the E (or S) step in this algorithm.

The EM algorithm is a widely applicable approach for computing maximum likelihood estimates for incomplete data. We present a stochastic approximation type EM algorithm: SAEM. This algorithm is an adaptation of the stochastic EM algorithm (SEM) that we have previously developed. Like SEM, SAEM overcomes most of the well-known limitations of EM.

The stochastic EM algorithm is particularly simple to apply to either linear or nonlinear mixed models with censoring. All that is required is a routine to simulate censored multinormal observations, and a routine to fit the desired uncensored mixed model.

A STOCHASTIC EM ALGORITHM FOR G-RHO FAMILY ACCELERATED FAILURE TIME MODEL WITH RANDOM EFFECTS KyungAh Im, PhD University of Pittsburgh, 2013 We propose an accelerated failure time model with random effects for correlated or clustered survival.