Probabilistic Programming Language and its Incremental Evaluation

2016 ◽  
pp. 357-376 ◽  
Author(s):  
Oleg Kiselyov
Keyword(s):  
Programming Language ◽  
Probabilistic Programming ◽  
Incremental Evaluation

scholarly journals Ranked Programming

2019 ◽  
Author(s):  
Tjitze Rienstra
Keyword(s):  
Probability Theory ◽  
Programming Language ◽  
Probabilistic Programming ◽  
Programming Methodology ◽  
Ranking Theory

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.

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

2019 ◽  
Vol 20 (1) ◽  
pp. 147-175 ◽  
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.

scholarly journals Automated learning with a probabilistic programming language: Birch

2018 ◽  
Vol 46 ◽  
pp. 29-43 ◽  
Author(s):  
Lawrence M. Murray ◽  
Thomas B. Schön
Keyword(s):  
Programming Language ◽  
Probabilistic Programming ◽  
Automated Learning

scholarly journals Statically bounded-memory delayed sampling for probabilistic streams

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-28
Author(s):  
Eric Atkinson ◽  
Guillaume Baudart ◽  
Louis Mandel ◽  
Charles Yuan ◽  
Michael Carbin
Keyword(s):  
Bayesian Inference ◽  
Programming Languages ◽  
Static Analysis ◽  
Programming Language ◽  
Inference Algorithm ◽  
Prior Work ◽  
Probabilistic Programming ◽  
The Core ◽  
Bounded Memory ◽  
Automated Inference

Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated inference. Prior work introduced a probabilistic programming language, ProbZelus, to extend probabilistic programming functionality to unbounded streams of data. This work demonstrated that the delayed sampling inference algorithm could be extended to work in a streaming context. ProbZelus showed that while delayed sampling could be effectively deployed on some programs, depending on the probabilistic model under consideration, delayed sampling is not guaranteed to use a bounded amount of memory over the course of the execution of the program. In this paper, we the present conditions on a probabilistic program’s execution under which delayed sampling will execute in bounded memory. The two conditions are dataflow properties of the core operations of delayed sampling: the m -consumed property and the unseparated paths property . A program executes in bounded memory under delayed sampling if, and only if, it satisfies the m -consumed and unseparated paths properties. We propose a static analysis that abstracts over these properties to soundly ensure that any program that passes the analysis satisfies these properties, and thus executes in bounded memory under delayed sampling.

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