Bayesian

In this text, I'll reveal tips on how to move from simply forecasting outcomes to actively intervening in systems to steer toward desired goals. With hands-on examples in predictive maintenance, I'll show how data-driven...

to tune hyperparamters of deep learning models (Keras Sequential model), compared with a conventional approach — Grid Search. Bayesian Optimization Bayesian Optimization is a sequential design strategy for global optimization of black-box functions. It is especially well-suited for...

In this text, I'll introduce you to hierarchical Bayesian (HB) modelling, a versatile approach to mechanically mix the outcomes of multiple sub-models. This method enables estimation of individual-level effects by optimally combining information across...

Is it higher than grid search?The use case might be reproduced with this notebook.I actually have created an example for instance the usefulness of the technique. Nevertheless, I actually have not been capable of...

Tips on how to construct a polls-only objective Bayesian model that goes from a state polling result in probability of winning the stateWith the presidential election approaching, a matter I, and I expect many...

Bayesian approaches have gotten increasingly popular but may be overwhelming at first. This extensive guide will walk you thru applications, libraries, and dependencies of causal discovery approaches.33 min read·13 hours agoThe countless possibilities of...

A workflow and code walkthrough for constructing a Bayesian regression model in STANNote: Try my previous article for a practical discussion on why Bayesian modeling could also be the appropriate selection in your task.This...

Gain higher insights out of your dataA/B testing, also often called split testing, allows businesses to experiment with different versions of a webpage or marketing asset to find out which one performs higher when...