A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...

It is hard to imagine anything more fascinating than automated systems that improve their own performance. The study of learning from data is commercially and scientifically important. This course is ...

Some of the material on this web page is based upon work supported by the National Science Foundation under Grants SES-0350686, SES-0719055, and . Any opinions, findings and conclusions or ...

Hierarchical dynamic model (HDM) is a probabilistic dynamic model which explicitly models spatial and temporal variations in the dynamic data. The temporal variation is handled in two aspects. First, ...

In the current emissions reduction scenario and transition toward a greener energy system, sustainable technology development has become key in every industrial sector. Nonetheless, the diffusion and ...

Here we duplicate a neural tracking paradigm, previously published with infants (aged 4 to 11 months), with adult participants, in order to explore potential developmental similarities and differences ...

Abstract: In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where ...

Divisive normalization is a ubiquitous computation commonly thought to be an implementation of the efficient coding principle. Despite empirical evidence that it reduces statistical redundancy present ...

Your browser does not support the audio element. Let’s take a closer look at different C++ libraries that can become useful to every data scientist for traditional ...

We present a tool for modeling the performance of methane leak detection and repair programs that can be used to evaluate the effectiveness of detection technologies and proposed mitigation policies.