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 ...
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 ...
CAMBRIDGE, Mass.--(BUSINESS WIRE)--Today, Gamalon, Inc. emerged from stealth mode to announce that it has developed a game-changing new approach to artificial intelligence/machine learning called ...
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 ...
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 ...
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 ...
J. P. Kaye, A. Majumdar, C. Gries, A. Buyantuyev, N. B. Grimm, D. Hope, G. D. Jenerette, W. X. Zhu and L. Baker Ecologists increasingly use plot-scale data to inform ...
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