Seminário

A New Algorithm for Inference in Hidden Markov Models with Lower Span Complexity

Sala P3.10, Instituto Superior Técnico (com transmissão via Zoom)

Por Diogo Pereira (CEMAT, Instituto Superior Técnico).

The maximum likelihood problem for Hidden Markov Models is usually numerically solved by the Baum-Welch algorithm, which uses the Expectation-Maximization algorithm to find the estimates of the parameters. This algorithm has a recursion depth equal to the data sample size and cannot be computed in parallel, which limits the use of modern GPUs to speed up computation time. A new algorithm is proposed that provides the same estimates as the Baum-Welch algorithm, requiring about the same number of iterations, but is designed in such a way that it can be parallelized. As a consequence, it leads to a significant reduction in the computation time. We illustrate this by means of numerical examples, where we consider simulated data as well as real datasets.


Transmissão por Zoom.

14h30
CEAUL - Centro de Estatística e Aplicações da Universidade de Lisboa / CEMAT - Centro de Matemática Computacional e Estocástica