Advanced Usage

How to Define a Customized Distribution

Turing.jl supports the use of distributions from the Distributions.jl package. By extension it also supports the use of customized distributions, by defining them as subtypes of Distribution type of the Distributions.jl package, as well as corresponding functions.

Below shows a workflow of how to define a customized distribution, using a flat prior as a simple example.

1. Define the Distribution Type

First, define a type of the distribution, as a subtype of a corresponding distribution type in the Distributions.jl package.

immutable Flat <: ContinuousUnivariateDistribution

2. Implement Sampling and Evaluation of the log-pdf

Second, define rand() and logpdf(), which will be used to run the model.

Distributions.rand(d::Flat) = rand()
Distributions.logpdf{T<:Real}(d::Flat, x::T) = zero(x)

3. Define Helper Functions

In most cases, it may be required to define helper functions, such as the minimum, maximum, rand, and logpdf functions, among others.

3.1 Domain Transformation

Some helper functions are necessary for domain transformation. For univariate distributions, the necessary ones to implement are minimum() and maximum().

Distributions.minimum(d::Flat) = -Inf
Distributions.maximum(d::Flat) = +Inf

Functions for domain transformation which may be required by multivariate or matrix-variate distributions are size(d), link(d, x) and invlink(d, x). Please see Turing’s transform.jl for examples.

3.2 Vectorization Support

The vectorization syntax follows rv ~ [distribution], which requires rand() and logpdf() to be called on multiple data points at once. An appropriate implementation for Flat are shown below.

Distributions.rand(d::Flat, n::Int) = Vector([rand() for _ = 1:n])
Distributions.logpdf{T<:Real}(d::Flat, x::Vector{T}) = zero(x)

Avoid Using the @model Macro

When integrating Turing.jl with other libraries, it can be necessary to avoid using the @model macro. To achieve this, one needs to understand the @model macro, which works as a closure and generates an amended function by

  1. assigning the arguments to corresponding local variables;
  2. adding two keyword arguments vi=VarInfo() and sampler=nothing to the scope; and
  3. forcing the function to return vi.

Thus by doing these three steps manually, one can get rid of the @model macro. Taking the gdemo model as an example, the two code sections below (macro and macro-free) are equivalent.

@model gdemo(x, y) = begin
    s ~ InverseGamma(2,3)
    m ~ Normal(0,sqrt(s))
    x ~ Normal(m, sqrt(s))
    x ~ Normal(m, sqrt(s))
    return s, m

mf = gdemo(1.5, 2.0)
sample(mf, HMC(1000, 0.1, 5))
# Force Turing.jl to initialize its compiler
mf(vi, sampler; x=[1.5, 2.0]) = begin
  s = Turing.assume(sampler,
                    InverseGamma(2, 3),
                    Turing.VarName(vi, [:c_s, :s], ""),
  m = Turing.assume(sampler,
                    Turing.VarName(vi, [:c_m, :m], ""),
  for i = 1:2
                   Normal(m, sqrt(s)),
mf() = mf(Turing.VarInfo(), nothing)

sample(mf, HMC(1000, 0.1, 5))

Note that the use of ~ must be removed due to the fact that in Julia 0.6, ~ is no longer a macro. For this reason, Turing.jl parses ~ within the @model macro to allow for this intuitive notation.

Task Copying

Turing copies Julia tasks to deliver efficient inference algorithms, but it also provides alternative slower implementation as a fallback. Task copying is enabled by default. Task copying requires building a small C program, which should be done automatically on Linux and Mac systems that have GCC and Make installed.