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

The successful implementation of Bayesian shrinkage analysis of high-dimensional regression models, as often encountered in quantitative trait locus (QTL) mapping, is contingent upon the choice of ...

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

Bayesian prior probabilities have an important place in probabilistic and statistical methods. In spite of this fact, the analysis of where these priors come from and how they are formed has received ...

Uncertainty in specification of the prior distribution is a common concern with Bayesian analysis. The robust Bayesian approach is to work with a class of prior distributions, which model uncertainty ...

A common misconception about Bayesian statistics is that it mainly involves incorporating personal prior beliefs or subjective opinions. While priors do play a role, the core strength of Bayesian ...

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 ...

Assume you made a decision to invest in an active strategy based on, say, a backtest of the underlying process (to be clear, active means NOT passive market-cap weight in my context). Over the next ...