2025-12-10 –, Analytics, Visualization & Decision Science
We often must make decisions under uncertainty—should you carry an umbrella if there's a 30 % chance of rain? Bayesian decision theory provides a principled, probabilistic framework to answer such questions by combining beliefs (probabilities), utilities (what matters to us), and actions to maximize expected gain.
This talk:
- Introduces key decision‑theoretic concepts in intuitive terms.
- Uses a toy umbrella example to ground ideas in relatable context.
- Demonstrates applications in Bayesian optimization (PoI/EI) and Bayesian experimental design.
- Is hands‑on—with Python code and practical tools—so participants leave ready to apply these ideas to real‑world problems.
This talk bridges everyday decision-making (umbrella example) with advanced techniques like Bayesian optimization and experimental design, and equips attendees with conceptual clarity and immediate code they can adapt to their data-driven workflows.
Audience
Primarily data scientists, ML practitioners, and statisticians who:
- Have applied Bayesian models but want a broader decision-theory perspective.
- Want actionable insight into uncertainty-aware decision frameworks.
- Seek practical demos in Python.
Outline
Motivation & Core Concepts (5 min)
- Frame real-world decision problems: rain or shine, clinical trials, A/B testing.
- Introduce Bayesian decision theory: beliefs × utilities → action via expected utility maximization.
Toy Example: Should I Bring an Umbrella? (8 min)
- Define: Probabilityp of rain; utility/loss matrix
| Action | Rain | No Rain |
|---|---|---|
| Umbrella | –1 (weight) | –1 (inconvenience) |
| No Umbrella | –10 (soaked) | 0 |
- Derive expected utility:
EU_umbrella = -1
EU_no_umbrella = -10p
So bring umbrella if p > 0.1.
- Interactive Python demo: explore how p and utility values shift the decision point.
Bayesian Optimization: PoI & EI (8 min)
- Introduce Gaussian-process-based optimization and the need to trade off exploration vs. exploitation.
- Define Probability of Improvement (PoI) and Expected Improvement (EI)
- Show how they're derived from decision theory: choosing the next point to maximize expected gain.
- Python demo using GPyTorch: fit GP, compute PoI/EI acquisition functions, visualize decision boundary—why one chooses a high-uncertainty point vs. one near known good values.
Bayesian Experimental Design (BED): Minimizing Uncertainty (8 min)
- Motivation: cost-sensitive data collection (labeling, surveys, medical tests).
- Define an information-based utility (e.g., expected reduction in entropy).
- Show how decision theory prescribes choosing the next experiment to maximize this expected utility.
- Python demo using OptBayesExpt.
Summary & Takeaways (1 min)
- Reiterate the decision-theoretic arc: belief → utility → action.
- Emphasize the unifying framework across umbrella example, optimization, and experimental design.
- Share resources & practical tips: GPyTorch / scikit-optimize, OptBayesExpt
Yes
