  We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic
model for time-dependent data assumed to have been generated by an unknown
number of latent features. This model is suitable as a prior in Bayesian
nonparametric feature allocation models in which the features underlying the
observed data exhibit a dependency structure over time. More specifically, we
establish a new framework for generating dependent Indian buffet processes,
where the Poisson random field model from population genetics is used as a way
of constructing dependent beta processes. Inference in the model is complex,
and we describe a sophisticated Markov Chain Monte Carlo algorithm for exact
posterior simulation. We apply our construction to develop a nonparametric
focused topic model for collections of time-stamped text documents and test it
on the full corpus of NIPS papers published from 1987 to 2015.
