MIT’s scientists claim they can teach a new concept to a computer using a single example rather than thousands. if confirmed, this significantly reduced the requirements needed for machine learning.

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 ...

1. Mark–recapture models are valuable for assessing diverse demographic and behavioural parameters, yet the precision of traditional estimates is often constrained by sparse empirical data. Bayesian ...

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...

Artificial intelligence is getting a boost in its ability to learn. On Tuesday, a company called Gamalon revealed a new technology for machine learning called Bayesian Program Synthesis (BPS). This ...

A novel Bayesian Hierarchical Network Model (BHNM) is designed for ensemble predictions of daily river stage, leveraging the spatial interdependence of river networks and hydrometeorological variables ...

Estimating abundance for multiple populations is of fundamental importance to many ecological monitoring programs. Equally important is quantifying the spatial distribution and characterizing the ...

We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...

Machine learning is all about getting computers to “understand” new concepts, but it’s still a pretty inefficient process, often requiring hundreds of examples for training. That may soon change, ...