Improved Expectation Maximization (EM) Algorithm based on Initial Parameter Selection

Expectation-Maximization (EM) algorithm

The Expectation Maximization (EM) algorithm

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...

A Note on the Expectation-Maximization (EM) Algorithm

The Expectation-Maximization (EM) algorithm is a general algorithm for maximum-likelihood estimation where the data are “incomplete” or the likelihood function involves latent variables. Note that the notion of “incomplete data” and “latent variables” are related: when we have a latent variable, we may regard our data as being incomplete since we do not observe values of the latent variables; s...

TRLFS: analysing spectra with an expectation-maximization (EM) algorithm.

A new approach for fitting statistical models to time-resolved laser-induced fluorescence spectroscopy (TRLFS) spectra is presented. Such spectra result from counting emitted photons in defined intervals. Any photon can be described by emission time and wavelength as observable attributes and by component and peak affiliation as hidden ones. Understanding the attribute values of the emitted pho...

Statistical analysis of neural data: The expectation-maximization (EM) algorithm