Book contents
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Semantics of Probabilistic Programming: A Gentle Introduction
- 2 Probabilistic Programs as Measures
- 3 Application ofComputable Distributions to the Semantics of Probabilistic Programs
- 4 On Probabilistic λ-Calculi
- 5 Probabilistic Couplings from Program Logics
- 6 Expected Runtime Analyis by Program Verification
- 7 Termination Analysis of Probabilistic Programs with Martingales
- 8 Quantitative Analysis of Programs with Probabilities and Concentration of Measure Inequalities
- 9 The Logical Essentials of Bayesian Reasoning
- 10 Quantitative Equational Reasoning
- 11 Probabilistic Abstract Interpretation: Sound Inference and Application to Privacy
- 12 Quantitative Information Flow with Monads in Haskell
- 13 Luck: A Probabilistic Language for Testing
- 14 Tabular: Probabilistic Inference from the Spreadsheet
- 15 Programming Unreliable Hardware
4 - On Probabilistic λ-Calculi
Published online by Cambridge University Press: 18 November 2020
- Frontmatter
- Contents
- List of Contributors
- Preface
- 1 Semantics of Probabilistic Programming: A Gentle Introduction
- 2 Probabilistic Programs as Measures
- 3 Application ofComputable Distributions to the Semantics of Probabilistic Programs
- 4 On Probabilistic λ-Calculi
- 5 Probabilistic Couplings from Program Logics
- 6 Expected Runtime Analyis by Program Verification
- 7 Termination Analysis of Probabilistic Programs with Martingales
- 8 Quantitative Analysis of Programs with Probabilities and Concentration of Measure Inequalities
- 9 The Logical Essentials of Bayesian Reasoning
- 10 Quantitative Equational Reasoning
- 11 Probabilistic Abstract Interpretation: Sound Inference and Application to Privacy
- 12 Quantitative Information Flow with Monads in Haskell
- 13 Luck: A Probabilistic Language for Testing
- 14 Tabular: Probabilistic Inference from the Spreadsheet
- 15 Programming Unreliable Hardware
Summary
Church's λ-calculus has become a universally accepted model of pure functional programming, and its properties have been thoroughly scrutinised by the research community in the last 90 years. Many variations of it have been introduced for the sake of capturing programming with various forms of effects, thus going beyond pure functional programming. This chapter is meant to be a gentle introduction to a family of such calculi, namely probabilistic λ-calculi, in their two main variations: randomised λ-calculi and Bayesian λ-calculi. We focus our attention on the operational semantics, expressive power and termination properties of randomised λ-calculi, only giving some hints and references about denotational models and Bayesian λ-calculi.
- Type
- Chapter
- Information
- Foundations of Probabilistic Programming , pp. 121 - 144Publisher: Cambridge University PressPrint publication year: 2020
- Creative Commons
- This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY 4.0 https://creativecommons.org/cclicenses/
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