We consider hypothesis testing and anomaly detection on datasets where each observation is a weighted network. Current approaches to hypothesis testing for weighted networks typically require thresholding the edge-weights, to transform the data to binary networks. This results in a loss of information, and outcomes are sensitive to choice of threshold levels. Our work avoids this, and we consider weighted-graph observations in two situations, 1) where each graph belongs to one of two populations, and 2) where entities belong to one of two populations, with each entity possessing multiple graphs (indexed e.g. by time). We propose a hierarchical Bayesian hypothesis testing framework that models each population with a mixture of latent space models for weighted networks, and then tests populations of networks for differences in distribution over components.Our framework is capable of population-level, entity-specific, as well as edge-specific hypothesis testing. We apply it to synthetic data and two real-world datasets: a social media dataset involving word co-occurrences from discussions on Twitter of the political unrest in Brazil, and a medical dataset involving fMRI brain-scans of human subjects. The results show that our proposed method has lower Type-I error and higher statistical power compared to previous alternatives that need to threshold the edge weights.

When the food supply finishes, or when the larvae of blowflies complete their development and migrate prior to the total removal of the larval substrate, they disperse to find adequate places for pupation, a process known as post-feeding larval dispersal. Based on experimental data of the initial and final configuration of the dispersion, the reproduction of such spatio-temporal behavior is achieved here by means of the evolutionary search for cellular automata with a distinct transition rule associated with each cell, also known as a nonuniform cellular automata, and with two states per cell in the lattice. Two-dimensional regular lattices and multivalued states will be considered and a practical question is the necessity of discovering a proper set of transition rules. Given that the number of rules is related to the number of cells in the lattice, the search space is very large and an evolution strategy is then considered to optimize the parameters of the transition rules, with two transition rules per cell. As the parameters to be optimized admit a physical interpretation, the obtained computational model can be analyzed to raise some hypothetical explanation of the observed spatio-temporal behavior.