It’s estimated that human adults make about 35,000 decisions a day — the percentage of good decisions depends on the adult. These choices can be as banal as deciding to roll or crumple toilet paper or ...

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 ...

What’s often misunderstood about Google’s incrementality testing and how Bayesian models use probability to guide better ...

The beauty of Bayesian sensors is that they can make your Home Assistant automations much more accurate. If your cat does sometimes set off your motion sensor, for example, you can't rely on the ...

Bayesian Networks, also known as Belief Networks or Bayes Nets, are a powerful probabilistic graphical model used for reasoning under uncertainty. They have been successfully applied to a wide range ...

For humans and machines, intelligence requires making sense of the world — inferring simple explanations for the mishmosh of information coming in through our senses, discovering regularities and ...

Humans live in a world of uncertainty. A shadowy figure on the sidewalk ahead could be a friend or a mugger. By flooring your car’s accelerator, you might beat the train to the intersection, or maybe ...

Learn how prior probability informs economic theory and decision-making in Bayesian statistics. Understand its role before collecting new data.