Receptive field sensitivity profiles of visual front-end cells in the LGN and V1 area in intact animals can be measured with increasing accuracy, both in the spatial and temporal domain. This urges the need for mathematical models. Scale-space theory, as a theory of (multiscale) apertures as operators on observed data, is concerned with the mathematical modeling of front-end visual system behav...

In the past decade, a number of advances in topic modeling have produced sophisticated models that are capable of generating hierarchies of topics. One challenge for these models is scalability: they are incapable of working at the massive scale of millions of documents and hundreds of thousands of terms. We address this challenge with a technique that learns a hierarchy of topics by iterativel...

We introduce a class of multi-scale models for random fields. The novel framework couples standard Markov models for the random field stochastic process at different levels of resolution, and links them via error models to induce a new and rich class of structured linear models reconciling modelling and information at different levels of resolution. Jeffrey’s rule of conditioning is used to rev...

data envelopment analysis (dea) is a non-parametric method for evaluating the relative technical efficiency for each member of a set of peer decision making units (dmus) with multiple inputs and multiple outputs. the original dea models use positive input and output variables that are measured on a ratio scale, but these models do not apply to the variables in which interval scale data can appe...

Common methods for spatial distribution, such as hydrologic response units, are subjective, time-consuming, and fail to capture the full range of basin attributes. Recent advances in statistical-learning techniques allow new approaches this problem. We propose use Gaussian Mixture Models (GMMs) distribution models. GMMs objectively select set modeling locations that best represent watershed fea...