Abstract: Naïve Bayesian inference enables classification or prediction of an event given observations of potentially contradictory evidences, and is particularly intriguing in power-limited contexts ...

Abstract: Accurate uncertainty quantification is critical for robust and trustworthy predictions in many real-world applications. Bayesian Neural Networks (BNNs) provide a principled approach for ...

The recovery operation of the superyacht Bayesian, sunk off the Sicilian coast, has been suspended after a diver died during the salvage efforts. The yacht, owned by Mike Lynch, sank a year ago in a ...

Bayesian methods for inference and prediction have become widespread in the social sciences (and beyond). Over the last decades, applied Bayesian modeling has evolved from a niche methodology with ...

Perceptual judgments of ambiguous stimuli are often biased by prior expectations. These biases may offer a window into the neural computations that give rise to perceptual interpretations of the ...

For whom? The events are open to all interested, within or outside of KI. The events are free of charge. The program is tailored towards users of statistics (but you don’t need to be a statistician), ...

ABSTRACT: This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, ...

This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, including ...

1 Cornell Center for Astrophysics and Planetary Science (CCAPS) and Department of Statistics and Data Science, Cornell University, Ithaca, NY, United States 2 Department of Statistical Science, Duke ...