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Bayesian Optimization DoE Python Examples

How Many Experiments Do I Really Need?

Active learning, domain expertise, or random selection: which gets you the best predictions from the fewest experiments?

All-Subset Regression

Instead of removing terms one by one, all-subset regression evaluates every possible combination of factors and picks the best model directly. Here's how it works and how it compares to ANOVA.

Foldover Designs

Learn how foldover designs resolve confounding in fractional factorial experiments by adding a mirror-image set of runs, with a practical example showing how a misleading effect gets unmasked.

Active Effects: Not Everything Matters Equally

Learn what active effects are, how to distinguish them from inactive ones, and why the principles of effect sparsity and effect hierarchy are the foundation of efficient experimental design.

Getting Started with Bayesian Optimization (BoFire)

A practical walkthrough of using the BoFire Python package to optimize chemical processes efficiently

Blocking in Practice: How to Actually Do It

Learn how blocking is implemented in experimental design through confounding, see a practical comparison of blocked vs. unblocked designs, and understand why randomization alone isn't enough.

Getting Started with Bayesian Optimization (NEXTorch)

A practical walkthrough of using the NEXTorch Python package to optimize chemical processes efficiently

Getting Started with Bayesian Optimization (BayBE)

A practical walkthrough of using the BayBE Python package to optimize chemical processes efficiently

A Technical Guide to Bayesian Optimization

How Bayesian Optimization decides which experiment to run next: surrogate models approximate your expensive experiments, and acquisition functions balance exploring the unknown with exploiting what looks promising.

Why You Should Always Code Your Variables

Learn why coding variables is essential in DoE. Discover how standardizing factor scales prevents misleading coefficients and enables accurate model interpretation.