We propose a Bayesian nonparametric model of multisensory perception based upon the Indian buffet process. The model includes a set of latent variables that learn multisensory features from unisensory data. The model is highly flexible because it makes few statistical assumptions. In particular, the number of latent multisensory features is not fixed a priori. Instead, this number is estimated ...

Learning in decentralized systems: A nonparametric approach by XuanLong Nguyen Doctor of Philosophy in Computer Science and the Designated Emphasis in Communication, Computation, and Statistics University of California, Berkeley Professor Michael I. Jordan, Chair Rapid advances in information technology result in increased deployment of decentralized decision-making systems embedded within larg...

A new identification algorithm for estimation of nonlinear characteristic in Wiener system with FIR linear element under existence of random noise is proposed. The assumptions imposed on unknown characteristic are weak. The algorithm is based on the idea of local median computing. The convergence in probability of the estimate is proved for each continuity point of the unknown characteristic. T...