📐 Introduction to Statistical Decision Theory PDF
By Silvia Bacci & Bruno Chiandotto
Utility theory and causal analysis for informed decision-making
✔ Statistical Theory | ✔ Decision Science | ✔ Instant PDF Download
Formal frameworks for optimal statistical decisions.
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Quickly locate definitions, theorems, and models.
About This Book
Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis by Silvia Bacci and Bruno Chiandotto provides a rigorous introduction to statistical decision-making, combining classical decision theory with modern approaches to causality and utility.
Published by CRC Press, this book is designed for advanced undergraduate and graduate students seeking a solid theoretical foundation in decision science and statistical inference.
What You Will Learn
- 🧠 Foundations of statistical decision theory
- 🎯 Utility functions and optimal decision rules
- 📊 Bayesian and frequentist decision frameworks
- 🔗 Causal analysis and decision-making
- 🎓 Formal reasoning under uncertainty
Key Topics Covered
- ✔ Loss functions and risk minimization
- ✔ Bayesian decision theory
- ✔ Utility theory and preference modeling
- ✔ Causal inference and graphical models
- ✔ Decision-making under uncertainty
Why the PDF Edition Is Ideal
- 💻 Access on laptop, tablet, or desktop
- 🖍 Annotate proofs, models, and definitions
- 🔍 Search equations and concepts instantly
- ⚡ Ideal for coursework, research, and exam prep
Who This Book Is For
- Graduate students in statistics and data science
- Students of decision theory and econometrics
- Researchers in causal inference
- Professionals in quantitative decision-making
Frequently Asked Questions
Is this an advanced-level book?
Yes. It is intended for advanced undergraduate and graduate-level readers.
Does it cover causal inference?
Yes. Causal analysis is a core component of the book.
Is the approach theoretical or applied?
It is primarily theoretical, with structured examples and models.
How do I receive the PDF?
Instant download access is provided after purchase.