Foundations of Probabilistic Programming
eBook - PDF

Foundations of Probabilistic Programming

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Foundations of Probabilistic Programming

About this book

What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs? This valuable guide covers such elementary questions and more. It provides a state-of-the-art overview of the theoretical underpinnings of modern probabilistic programming and their applications in machine learning, security, and other domains, at a level suitable for graduate students and non-experts in the field. In addition, the book treats the connection between probabilistic programs and mathematical logic, security (what is the probability that software leaks confidential information?), and presents three programming languages for different applications: Excel tables, program testing, and approximate computing. This title is also available as Open Access on Cambridge Core.

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Yes, you can access Foundations of Probabilistic Programming by Gilles Barthe,Joost-Pieter Katoen,Alexandra Silva in PDF and/or ePUB format, as well as other popular books in Computer Science & Programming Languages. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-title
  3. Title page
  4. Copyright information
  5. Contents
  6. Contributors
  7. Preface
  8. 1 Semantics of Probabilistic Programming: A Gentle Introduction
  9. 2 Probabilistic Programs as Measures
  10. 3 An Application of Computable Distributions to the Semantics of Probabilistic Programs
  11. 4 On Probabilistic λ-Calculi
  12. 5 Probabilistic Couplings from Program Logics
  13. 6 Expected Runtime Analysis by Program Verification
  14. 7 Termination Analysis of Probabilistic Programs with Martingales
  15. 8 Quantitative Analysis of Programs with Probabilities and Concentration of Measure Inequalities
  16. 9 The Logical Essentials of Bayesian Reasoning
  17. 10 Quantitative Equational Reasoning
  18. 11 Probabilistic Abstract Interpretation: Sound Inference and Application to Privacy
  19. 12 Quantitative Information Flow with Monads in Haskell
  20. 13 Luck: A Probabilistic Language for Testing
  21. 14 Tabular: Probabilistic Inference from the Spreadsheet
  22. 15 Programming Unreliable Hardware