Automated learning with a probabilistic programming language: Birch

10/02/2018
by   Lawrence M. Murray, et al.
0

This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We consider a typical workflow and how probabilistic programming languages can help to automate this workflow, especially in the matching of models with inference methods. We focus on two properties of a model that are critical in this matching: its structure---the conditional dependencies between random variables---and its form---the precise mathematical definition of those dependencies. While the structure and form of a probabilistic model are often fixed a priori, it is a curiosity of probabilistic programming that they need not be, and may instead vary according to random choices made during program execution. We introduce a formal description of models expressed as programs, and discuss some of the ways in which probabilistic programming languages can reveal the structure and form of these, in order to tailor inference methods. We demonstrate the ideas with a new probabilistic programming language called Birch, with a multiple object tracking example.

READ FULL TEXT
research
11/07/2018

On the extreme power of nonstandard programming languages

Suenaga and Hasuo introduced a nonstandard programming language While^ ...
research
04/08/2021

Sound Probabilistic Inference via Guide Types

Probabilistic programming languages aim to describe and automate Bayesia...
research
06/09/2021

Expectation Programming

Building on ideas from probabilistic programming, we introduce the conce...
research
06/30/2016

Swift: Compiled Inference for Probabilistic Programming Languages

A probabilistic program defines a probability measure over its semantic ...
research
01/13/2017

Deep Probabilistic Programming

We propose Edward, a Turing-complete probabilistic programming language....
research
07/21/2022

Language Model Cascades

Prompted models have demonstrated impressive few-shot learning abilities...
research
10/22/2020

Conditional independence by typing

A central goal of probabilistic programming languages (PPLs) is to separ...

Please sign up or login with your details

Forgot password? Click here to reset