Expectation Programming

06/09/2021
by   Tim Reichelt, et al.
0

Building on ideas from probabilistic programming, we introduce the concept of an expectation programming framework (EPF) that automates the calculation of expectations. Analogous to a probabilistic program, an expectation program is comprised of a mix of probabilistic constructs and deterministic calculations that define a conditional distribution over its variables. However, the focus of the inference engine in an EPF is to directly estimate the resulting expectation of the program return values, rather than approximate the conditional distribution itself. This distinction allows us to achieve substantial performance improvements over the standard probabilistic programming pipeline by tailoring the inference to the precise expectation we care about. We realize a particular instantiation of our EPF concept by extending the probabilistic programming language Turing to allow so-called target-aware inference to be run automatically, and show that this leads to significant empirical gains compared to conventional posterior-based inference.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 19

page 20

03/25/2019

The Random Conditional Distribution for Higher-Order Probabilistic Inference

The need to condition distributional properties such as expectation, var...
09/28/2021

Expectation-based Minimalist Grammars

Expectation-based Minimalist Grammars (e-MGs) are simplified versions of...
10/02/2018

Automated learning with a probabilistic programming language: Birch

This work offers a broad perspective on probabilistic modeling and infer...
10/31/2016

Inference Compilation and Universal Probabilistic Programming

We introduce a method for using deep neural networks to amortize the cos...
04/06/2022

Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming

We propose a new method to approximate the posterior distribution of pro...
04/11/2018

Compositional semantics for new paradigms: probabilistic, hybrid and beyond

Emerging computational paradigms, such as probabilistic and hybrid progr...
06/09/2021

Data-Driven Invariant Learning for Probabilistic Programs

Morgan and McIver's weakest pre-expectation framework is one of the most...

Code Repositories

ExpectationProgrammingExperiments

None


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.