In this paper, we derive a new class of finite-dimensional filters for integrals and stochastic integrals of moments of the state for continuous-time linear Gaussian systems. Apart from being of significant mathematical interest, these new filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the model parameters.

The Expectation/Conditional Maximisation Either (ECME) algorithm has proven to be an effective way of accelerating the Expectation Maximisation (EM) algorithm for many problems. Recognising the limitation of using prefixed acceleration subspaces in ECME, we propose a Dynamic ECME (DECME) algorithm which allows the acceleration subspaces to be chosen dynamically. The simplest DECME implementatio...

In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters. We also propose a sequential Monte Carlo approximation of our online EM algorithm. We show the performance of the proposed method ...

We propose a new lower bound on the independence number of a graph. We show that our bound compares favorably to recent ones (e.g. [12]). We obtain our bound by using the Bhatia-Davis inequality applied with analytical results (minimum, maximum, expectation and variance) of an algorithm for the vertex cover problem.

This paper presents a strategy for combining the results of image classification and image segmentation. The visual features used for classification and segmentation may be different in general. Fusion is performed in a Maximum Likelihood framework using the Expectation Maximization algorithm. Preliminary results show that segmentation may effectively contribute to increase the quality of class...

We present a parameter inference algorithm for autonomous stochastic linear hybrid systems, which computes a maximum-likelihood model, given only a set of continuous output data of the system. We overcome the potentially intractable problem of identifying the sequence of discrete modes by using dynamic programming; we then compute the maximum-likelihood continuous models using an Expectation Ma...

Biological processes measured repeatedly among a series of individuals are standardly analyzed by mixed models. These biological processes can be adequately modeled by parametric Stochastic Differential Equations (SDEs). We focus on the parametric maximum likelihood estimation of this mixed-effects model defined by SDE. As the likelihood is not explicit, we propose a stochastic version of the E...

We perform sequence estimation for CPM signals transmitted in a time varying multipath channel. The EM (Expectation-Maximization) algorithm, an iterative proce dure for producing maximum likelihood estimates, is applied to handle the unknown channel. In order to enable implementation of the EM algorithm in this system, a simplification of this algorithm is derived. Channel estimates derived fro...