It is interesting to note that in the algorithm of Tagare et al. (2015 ▸) the latent variables (representing the contributions of the principal components to each particle) are marginalized. Because of this marginalization, the number of unknown parameters that need to be estimated (the principal components and variances) is fixed and does not change with the number of particles. We have found this feature to be very valuable for relatively small sets of images (say 100 000 images), which is the case in our work, in order to prevent the number of parameters to be estimated growing with the number of particles. Statistically speaking, nonmarginalization is known to be a problem when there are few particles, where the estimates can be unreliable. Since the method developed by Tagare and coworkers does not suffer from this, we chose this method.