A Tutorial on Bayesian Optimization

Source: Dev.to

Overview

Bayesian Optimization is a smart approach for finding optimal settings when each experiment is slow or costly. It reduces the number of trials needed by intelligently selecting the most promising points to evaluate.

How It Works

1. Surrogate Model – A simple model (often a Gaussian Process) approximates the true objective function based on the results of previous trials.
2. Uncertainty Quantification – The surrogate model provides an estimate of uncertainty, indicating where the model is less confident.
3. Acquisition Function – Using the surrogate and its uncertainty, an acquisition function decides the next point to evaluate, balancing exploration (trying uncertain regions) and exploitation (refining known good areas).
4. Iterative Loop – The process repeats: evaluate the chosen point, update the surrogate model, and select the next point.

Benefits

• Fewer Experiments – Achieves good solutions with far fewer evaluations compared to random or grid search.
• Handles Noisy Data – Can work with noisy or imperfect measurements.
• Parallel Evaluations – Supports running multiple trials simultaneously to speed up the overall process.
• Adaptable – Remains effective even when conditions change slightly during optimization.

Applications

• Hyperparameter tuning for machine‑learning models.
• Engineering design where simulations are expensive.
• A/B testing in marketing when each test incurs significant cost or time.
• Any domain where real‑world experiments are slow, costly, or limited.

Further Reading

A Tutorial on Bayesian Optimization – Paperium.net.