Non-Stationary Bayesian Learning for Global Sustainability

Learning Non-Stationary Dynamic Bayesian Networks

Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional DBN structure learning is that the data are generated by a stationary process, an assumption that is not true in many important settings. In this paper, we introduce a new class of graphical model called a nonstationary...

Learning Continuous Time Bayesian Networks in Non-stationary Domains

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node to change over continuous time. Three settings are developed for learning non-stationary continuous time Bayesian networks from data: known transition times, known number of epochs and unknown number of epochs. A score function for each setting is derived and the corresponding learning algor...

Bayesian Structure Learning for Stationary Time Series

While much work has explored probabilistic graphical models for independent data, less attention has been paid to time series. The goal in this setting is to determine conditional independence relations between entire time series, which for stationary series, are encoded by zeros in the inverse spectral density matrix. We take a Bayesian approach to structure learning, placing priors on (i) the...

Non-stationary Clustering Bayesian Networks for glaucoma

Glaucoma is a major cause of blindness and its mechanisms are not fully understood. The progression of the disease can be slowed by early diagnosis, but this is a difficult task because available data is typically noisy and has high variability. Several artificial intelligence approaches have been used in this context, although they generally don’t exploit the temporal nature of the data. Here ...

Non - Stationary Bayesian Modelling and