Interpreting lion behaviour with nonparametric probabilistic programs - Dhir, N; Wood, F; Vakar, M; Markham, A; Wijers, M; Trethowan, P; Du Preez, B; Loveridge, A; Macdonald, D (Association for Uncertainty in Artificial Intelligence, 2017)
We consider the problem of unsupervised learning of meaningful behavioural segments of high-dimensional time-series observations, collected from a pride of African lions. We demonstrate, by way of probabilistic programming, a methodology which allows for quick iteration over models and Bayesian inferences, which enables us to learn meaningful behavioural segments. We introduce a new Bayesian nonparametric (BNP) state-space model, which extends the HDP-HMM with an explicit BNP treatment of duration distributions, to deal with different levels of granularity of the latent behavioural space of the lions. Furthermore, we combine this approach with unsupervised feature learning, using variational autoencoders.