The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

Even in this day and age, computer learning is far behind the learning capability of humans. A team of researchers seek to shrink the gap, however, developing a technique called “Bayesian Program ...

This illustration gives a sense of how characters from alphabets around the world were replicated through human vs. machine learning. (Credit: Danqing Wang) Researchers say they’ve developed an ...

An app developed by Gamalon recognizes objects after seeing a few examples. A learning program recognizes simpler concepts such as lines and rectangles. Machine learning is becoming extremely powerful ...

The old adage that practice makes perfect applies to machines as well, as many of today’s artificially intelligent devices rely on repetition to learn. Deep-learning algorithms are designed to allow ...

This is a preview. Log in through your library . Abstract This paper uses semidefinite programming (SDP) to construct Bayesian optimal design for nonlinear regression models. The setup here extends ...

Evaluating the impact of program activities on outcomes occasionally involves complex data structures – such as units nested within clusters, and observed longitudinally – and the corresponding ...

We present a spatial Bayesian hierarchical model for seasonal extreme precipitation. At the first level of hierarchy, the seasonal maximum precipitation (i.e. block maxima) at any location is assumed ...