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GitHub

API

Part of the API of DynamicPPL is defined in the more lightweight interface package AbstractPPL.jl and reexported here.

Model

Macros

A core component of DynamicPPL is the @model macro. It can be used to define probabilistic models in an intuitive way by specifying random variables and their distributions with ~ statements. These statements are rewritten by @model as calls of internal functions for sampling the variables and computing their log densities.

DynamicPPL.@model — Macro
@model(expr[, warn = false])

Macro to specify a probabilistic model.

If warn is true, a warning is displayed if internal variable names are used in the model definition.

Examples

Model definition:

@model function model(x, y = 42)
     ...
 end

To generate a Model, call model(xvalue) or model(xvalue, yvalue).

source

One can nest models and call another model inside the model function with @submodel.

DynamicPPL.@submodel — Macro
@submodel model
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GitHub
DynamicPPL.jl
A domain-specific language and backend for probabilistic programming languages, used by Turing.jl.

 
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GitHub
Transforming variables
Motivation
In a probabilistic programming language (PPL) such as DynamicPPL.jl, one crucial functionality for enabling a large number of inference algorithms to be implemented, in particular gradient-based ones, is the ability to work with "unconstrained" variables.
For example, consider the following model:
@model function demo()
     s ~ InverseGamma(2, 3)
     return m ~ Normal(0, √s)