This dissertation shows that careful injection of noise into sample data can substantially speed up Expectation-Maximization algorithms. Expectation-Maximization algorithms are a class of iterative algorithms for extracting maximum likelihood estimates from corrupted or incomplete data. The convergence speed-up is an example of a noise benefit or"stochastic resonance"in statistical signal proce...

The Expectation-Maximization Algorithm (EM) is a widely used method allowing to estimate the maximum likelihood of models involving latent variables. When Expectation step cannot be computed easily, one can use stochastic versions EM such as Stochastic Approximation EM. This algorithm, however, has drawback require joint belong curved exponential family. To overcome this problem, [16] introduce...

A maximum likelihood approach to emission image reconstruction from projections. Maximum likelihood from incomplete data via the EM algorithm. " A theoretical study of some maximum likelihood algorithms for emission and transmission tomography. Attenuation compensation of cone beam SPECT images using maximum likelihood reconstruction. Three-dimensional SPECT reconstruction of combined cone beam...

This paper focuses on the Bayesian posterior mean estimates (or Bayes’ estimate) of the parameter set of Poisson hidden Markov models in which the observation sequence is generated by a Poisson distribution whose parameter depends on the underlining discrete-time time-homogeneous Markov chain. Although the most commonly used procedures for obtaining parameter estimates for hidden Markov models ...

This paper studies the regularization of constrained Maximum Likelihood iterative algorithms applied to incompatible ill-posed linear inverse problems. Specifically we introduce a novel stopping rule which defines a regularization algorithm for the Iterative Space Reconstruction Algorithm in the case of Least-Squares minimization. Further we show that the same rule regularizes the Expectation M...

A novel maximum likelihood solution to the problem of identifying parameters of a nonlinear model under missing observations is presented. An expectation maximization (EM) algorithm, which uses the expected value of the complete log-likelihood function including the missing observations, is developed. The expected value of the complete log-likelihood (E-step) in the EM algorithm is approximated...

In this note we introduce a new randomized algorithm for counting triangles in graphs. We show that under mild conditions, the estimate of our algorithm is strongly concentrated around the true number of triangles. Specifically, if p ≥ max ( log n t , log n √ t ), where n, t, ∆ denote the number of vertices in G, the number of triangles in G, the maximum number of triangles an edge of G is cont...

The Iterated Extended Kalman smoother (IEKS) is shown to be equivalent to one iteration of the Expectation Maximisation (EM)-based SAGE algorithm for the class of nonlinear signal models containing polynomial dynamics. Thus the IEKS is a maximum a posteriori (MAP) state sequence estimator for this class of systems. The Iterated Extended Kalman filter (IEKF) can be thought of as a heuristic, onl...

Wireless Sensor Networks (WSNs) are mainly deployed for data acquisition, thus, the network performance can be passively measured by exploiting whether application data from various sensor nodes reach the sink. In this paper, therefore, we take into account the unique data aggregation communication paradigm of WSNs and model the problem of link loss rates inference as a Maximum-Likelihood Estim...