PyAutoFit: A Classy Probabilistic Programming Language for Model Composition and Fitting

While probabilistic programming is a powerful tool, uncertainty is not always of a probabilistic kind. Some types of uncertainty are better captured using ranking theory, which is an alternative to probability theory where uncertainty is measured using degrees of surprise on the integer scale from 0 to ∞. In this paper we combine probabilistic programming methodology with ranking theory and develop a ranked programming language. We use the Scheme programming language a basis and extend it with the ability to express both normal and exceptional behaviour of a model, and perform inference on such models. Like probabilistic programming, our approach provides a simple and flexible way to represent and reason with models involving uncertainty, but using a coarser grained and computationally simpler kind of uncertainty.

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A Probabilistic Programming Language for Influence Diagrams

Lecture Notes in Computer Science - Scalable Uncertainty Management ◽

10.1007/978-3-319-67582-4_18 ◽

2017 ◽

pp. 252-265

Author(s):

Steven D. Prestwich ◽

Federico Toffano ◽

Nic Wilson

Keyword(s):

Programming Language ◽

Influence Diagrams ◽

Probabilistic Programming

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Implementing a Library for Probabilistic Programming Using Non-strict Non-determinism

Theory and Practice of Logic Programming ◽

10.1017/s1471068419000085 ◽

2019 ◽

Vol 20 (1) ◽

pp. 147-175 ◽

Cited By ~ 1

Author(s):

SANDRA DYLUS ◽

JAN CHRISTIANSEN ◽

FINN TEEGEN

Keyword(s):

Logic Programming ◽

Programming Languages ◽

Programming Language ◽

Probabilistic Choice ◽

Probabilistic Programming ◽

Logic Programming Language ◽

Functional Logic Programming ◽

Functional Logic ◽

Language Characteristics ◽

Standard List

AbstractThis paper presentsPFLP, a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming. In fact, the paradigms of functional logic and probabilistic programming are closely connected. That is, language characteristics from one area exist in the other and vice versa. For example, the concepts of non-deterministic choice and call-time choice as known from functional logic programming are related to and coincide with stochastic memoization and probabilistic choice in probabilistic programming, respectively. We will further see that an implementation based on the concepts of functional logic programming can have benefits with respect to performance compared to a standard list-based implementation and can even compete with full-blown probabilistic programming languages, which we illustrate by several benchmarks.

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