We propose a mathematical formalisation of the ``wave model'' originally developed in historical linguistics but with further applications in human sciences. This model assumes new traits appear in a population and spread to nearby populations depending on their closeness. It is mostly used to describe joint evolution of closely related populations, for example of several dialects. These situations of permanent contact are not accurately represented by its competitors based on tree structures. We built a fully Bayesian generative model where innovation spread along a fixed graph and disappear according to a death process. We then develop a Metropolis-Hastings within Gibbs sampler to sample from the posterior distribution on the graph. We test our method on simulated datasets as well as on several real dataset.

Scholars have debated naturalistic theories of religion for thousands of years, but only recently have scientists begun to test predictions empirically. Existing databases contain few variables on rel...

Dans la mythologie publicitaire des "agents", des logiciels en théorie capables d'effectuer des tâches à notre place, l'oligopole de la Silicon Valley dessine en creux un monde peuplé d'individus…

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Le premier ministre a déclaré, mardi 11 février à l’Assemblée, n’avoir « jamais » été informé des violences commises dans cet établissement catholique de la région de Pau. Mediapart publie un courrie…