In information theory, and more specifically in statistical machine learning and signal processing, mean field particle methods are used to sample sequentially from the conditional distributions of some random process with respect to a sequence of observations or a cascade of rare events. In discrete time nonlinear filtering problems, the conditional distributions of the random states of a signal given partial and noisy observations satisfy a nonlinear updating-prediction evolution equation. The updating step is given by Bayes' rule, and the prediction step is a Chapman-Kolmogorov transport equation. The mean field particle interpretation of these nonlinear filtering equations is a genetic type selection-mutation particle algorithm During the mutation step, the particles evolve independently of one another according to the Markov transitions of the signal. During the selection stage, particles with small relative likelihood values are killed, while the ones with high relative values are multiplied. These mean field particle techniques are also used to solve multiple-object tracking problems, and more specifically to estimate association measures