Incorporating non-stationary spatial variability into dynamic species distribution models

Incorporating Resilience into Dynamic Social Models

Incorporating Non-sequential interactions into Click Models

Click-through information is considered as a valuable source of users’ implicit relevance feedback. As user behavior is usually influenced by a number of factors such as position, presentation style and site reputation, researchers have proposed a variety of assumptions (i.e. click models) to generate a reasonable estimation of result relevance. The construction of click models usually follow s...

Non-stationary dynamic Bayesian networks

Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assumes that the data are generated by a stationary process. However, there are interesting and important circumstances where that assumption will not hold and potential non-stationarity cannot be ignored. Here we introduce a new cl...

Incorporating Spatial Similarity into Ensemble Clustering

This paper addresses a fundamental problem in ensemble clustering – namely, how should one compare the similarity of two clusterings? The vast majority of prior techniques for comparing clusterings are entirely partitional, i.e., they examine assignments of points in set theoretic terms after they have been partitioned. In doing so, these methods ignore the spatial layout of the data, disregard...

Incorporating uncertainty in predictive species distribution modelling.

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often u...