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In this tutorial we develop a very simple probabilistic programming language. The implementation is similar to DynamicPPL. This is intentional as we want to demonstrate some key ideas from Turing's internal implementation.

To make things easy to understand and to implement we restrict our language to a very simple subset of the language that Turing actually supports. Defining an accurate syntax description is not our goal here, instead, we give a simple example and all similar programs should work.

Consider a probabilistic model defined by

$$ \begin{aligned} a &\sim \operatorname{Normal}(0.5, 1^2) \ b &\sim \operatorname{Normal}(a, 2^2) \ x &\sim \operatorname{Normal}(b, 0.5^2) \end{aligned} $$

We assume that x is data, i.e., an observed variable. In our small language this model will be defined as

@mini_model function m(x)
    a ~ Normal(0.5, 1)
    b ~ Normal(a, 2)
    x ~ Normal(b, 0.5)
    return nothing

Specifically, we demand that

  • all observed variables are arguments of the program,
  • the model definition does not contain any control flow,
  • all variables are scalars, and
  • the function returns nothing.

First, we import some required packages:

using MacroTools, Distributions, Random, AbstractMCMC, MCMCChains

Before getting to the actual "compiler", we first build the data structure for the program trace. A program trace for a probabilistic programming language needs to at least record the values of stochastic variables and their log-probabilities.

struct VarInfo{V,L}

VarInfo() = VarInfo(Dict{Symbol,Float64}(), Dict{Symbol,Float64}())

function Base.setindex!(varinfo::VarInfo, (value, logp), var_id)
    varinfo.values[var_id] = value
    varinfo.logps[var_id] = logp
    return varinfo

Internally, our probabilistic programming language works with two main functions:

  • assume for sampling unobserved variables and computing their log-probabilities, and
  • observe for computing log-probabilities of observed variables (but not sampling them).

For different inference algorithms we may have to use different sampling procedures and different log-probability computations. For instance, in some cases we might want to sample all variables from their prior distributions and in other cases we might only want to compute the log-likelihood of the observations based on a given set of values for the unobserved variables. Thus depending on the inference algorithm we want to use different assume and observe implementations. We can achieve this by providing this context information as a function argument to assume and observe.

Note: Although the context system in this tutorial is inspired by DynamicPPL, Turing's context system is much more complicated for flexibility and efficiency reasons. Thus readers are advised to refer to the documentation of DynamicPPL and Turing for more detailed information about their context system.

Here we can see the implementation of a sampler that draws values of unobserved variables from the prior and computes the log-probability for every variable.

struct SamplingContext{S<:AbstractMCMC.AbstractSampler,R<:Random.AbstractRNG}

struct PriorSampler <: AbstractMCMC.AbstractSampler end

function observe(context::SamplingContext, varinfo, dist, var_id, var_value)
    logp = logpdf(dist, var_value)
    varinfo[var_id] = (var_value, logp)
    return nothing

function assume(context::SamplingContext{PriorSampler}, varinfo, dist, var_id)
    sample = Random.rand(context.rng, dist)
    logp = logpdf(dist, sample)
    varinfo[var_id] = (sample, logp)
    return sample

Next we define the "compiler" for our simple programming language. The term compiler is actually a bit misleading here since its only purpose is to transform the function definition in the @mini_model macro by

  • adding the context information (context) and the tracing data structure (varinfo) as additional arguments, and
  • replacing tildes with calls to assume and observe.

Afterwards, as usual the Julia compiler will just-in-time compile the model function when it is called.

The manipulation of Julia expressions is an advanced part of the Julia language. The Julia documentation provides an introduction to and more details about this so-called metaprogramming.

macro mini_model(expr)
    return esc(mini_model(expr))

function mini_model(expr)
    # Split the function definition into a dictionary with its name, arguments, body etc.
    def = MacroTools.splitdef(expr)

    # Replace tildes in the function body with calls to `assume` or `observe`
    def[:body] = MacroTools.postwalk(def[:body]) do sub_expr
        if MacroTools.@capture(sub_expr, var_ ~ dist_)
            if var in def[:args]
                # If the variable is an argument of the model function, it is observed
                return :($(observe)(context, varinfo, $dist, $(Meta.quot(var)), $var))
                # Otherwise it is unobserved
                return :($var = $(assume)(context, varinfo, $dist, $(Meta.quot(var))))
            return sub_expr

    # Add `context` and `varinfo` arguments to the model function
    def[:args] = vcat(:varinfo, :context, def[:args])

    # Reassemble the function definition from its name, arguments, body etc.
    return MacroTools.combinedef(def)

For inference, we make use of the AbstractMCMC interface. It provides a default implementation of a sample function for sampling a Markov chain. The default implementation already supports e.g. sampling of multiple chains in parallel, thinning of samples, or discarding initial samples.

The AbstractMCMC interface requires us to at least

  • define a model that is a subtype of AbstractMCMC.AbstractModel,
  • define a sampler that is a subtype of AbstractMCMC.AbstractSampler,
  • implement AbstractMCMC.step for our model and sampler.

Thus here we define a MiniModel model. In this model we store the model function and the observed data.

struct MiniModel{F,D} <: AbstractMCMC.AbstractModel
    data::D # a NamedTuple of all the data

In the Turing compiler, the model-specific DynamicPPL.Model is constructed automatically when calling the model function. But for the sake of simplicity here we construct the model manually.

To illustrate probabilistic inference with our mini language we implement an extremely simplistic Random-Walk Metropolis-Hastings sampler. We hard-code the proposal step as part of the sampler and only allow normal distributions with zero mean and fixed standard deviation. The Metropolis-Hastings sampler in Turing is more flexible.

struct MHSampler{T<:Real} <: AbstractMCMC.AbstractSampler

MHSampler() = MHSampler(1)

function assume(context::SamplingContext{<:MHSampler}, varinfo, dist, var_id)
    sampler = context.sampler
    old_value = varinfo.values[var_id]

    # propose a random-walk step, i.e, add the current value to a random 
    # value sampled from a Normal distribution centered at 0
    value = rand(context.rng, Normal(old_value, sampler.sigma))
    logp = Distributions.logpdf(dist, value)
    varinfo[var_id] = (value, logp)

    return value

We need to define two step functions, one for the first step and the other for the following steps. In the first step we sample values from the prior distributions and in the following steps we sample with the random-walk proposal. The two functions are identified by the different arguments they take.

# The fist step: Sampling from the prior distributions
function AbstractMCMC.step(
    rng::Random.AbstractRNG, model::MiniModel, sampler::MHSampler; kwargs...
    vi = VarInfo()
    ctx = SamplingContext(rng, PriorSampler())
    model.f(vi, ctx, values(
    return vi, vi

# The following steps: Sampling with random-walk proposal
function AbstractMCMC.step(
    prev_state::VarInfo; # is just the old trace
    vi = prev_state
    new_vi = deepcopy(vi)
    ctx = SamplingContext(rng, sampler)
    model.f(new_vi, ctx, values(

    # Compute log acceptance probability
    # Since the proposal is symmetric the computation can be simplified
    logα = sum(values(new_vi.logps)) - sum(values(vi.logps))

    # Accept proposal with computed acceptance probability
    if -randexp(rng) < logα
        return new_vi, new_vi
        return prev_state, prev_state

To make it easier to analyze the samples and compare them with results from Turing, additionally we define a version of AbstractMCMC.bundle_samples for our model and sampler that returns a MCMCChains.Chains object of samples.

function AbstractMCMC.bundle_samples(
    samples, model::MiniModel, ::MHSampler, ::Any, ::Type{Chains}; kwargs...
    # We get a vector of traces
    values = [sample.values for sample in samples]
    params = [key for key in keys(values[1]) if key  keys(]
    vals = reduce(hcat, [value[p] for value in values] for p in params)
    # Composing the `Chains` data-structure, of which analyzing infrastructure is provided
    chains = Chains(vals, params)
    return chains

Let us check how our mini probabilistic programming language works. We define the probabilistic model:

@mini_model function m(x)
    a ~ Normal(0.5, 1)
    b ~ Normal(a, 2)
    x ~ Normal(b, 0.5)
    return nothing

We perform inference with data x = 3.0:

sample(MiniModel(m, (x=3.0,)), MHSampler(), 1_000_000; chain_type=Chains)
Chains MCMC chain (1000000×2×1 Array{Float64, 3}):

Iterations        = 1:1:1000000
Number of chains  = 1
Samples per chain = 1000000
parameters        = a, b

Summary Statistics
  parameters      mean       std      mcse      ess_bulk      ess_tail     
 rh ⋯
      Symbol   Float64   Float64   Float64       Float64       Float64   Fl
oat ⋯

           a    0.9759    0.8966    0.0031    83538.5328   120494.6047    1
.00 ⋯
           b    2.8801    0.4882    0.0012   172239.1328   211727.9896    1
.00 ⋯
                                                               2 columns om

  parameters      2.5%     25.0%     50.0%     75.0%     97.5%
      Symbol   Float64   Float64   Float64   Float64   Float64

           a   -0.7830    0.3701    0.9742    1.5802    2.7324
           b    1.9207    2.5501    2.8802    3.2109    3.8370

We compare these results with Turing.

using Turing
using PDMats

@model function turing_m(x)
    a ~ Normal(0.5, 1)
    b ~ Normal(a, 2)
    x ~ Normal(b, 0.5)
    return nothing

sample(turing_m(3.0), MH(ScalMat(2, 1.0)), 1_000_000)
Chains MCMC chain (1000000×3×1 Array{Float64, 3}):

Iterations        = 1:1:1000000
Number of chains  = 1
Samples per chain = 1000000
Wall duration     = 4.72 seconds
Compute duration  = 4.72 seconds
parameters        = a, b
internals         = lp

Summary Statistics
  parameters      mean       std      mcse      ess_bulk      ess_tail     
 rh ⋯
      Symbol   Float64   Float64   Float64       Float64       Float64   Fl
oat ⋯

           a    0.9783    0.8988    0.0032    78992.7518   124721.4606    1
.00 ⋯
           b    2.8808    0.4865    0.0012   176346.1535   216272.7460    1
.00 ⋯
                                                               2 columns om

  parameters      2.5%     25.0%     50.0%     75.0%     97.5%
      Symbol   Float64   Float64   Float64   Float64   Float64

           a   -0.7834    0.3704    0.9781    1.5831    2.7409
           b    1.9254    2.5530    2.8804    3.2086    3.8321

As you can see, with our simple probabilistic programming language and custom samplers we get similar results as Turing.


These tutorials are a part of the TuringTutorials repository, found at:

To locally run this tutorial, do the following commands:

using TuringTutorials
TuringTutorials.weave("14-minituring", "14_minituring.jmd")

Computer Information:

Julia Version 1.9.2
Commit e4ee485e909 (2023-07-05 09:39 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 128 × AMD EPYC 7502 32-Core Processor
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, znver2)
  Threads: 1 on 16 virtual cores
  JULIA_DEPOT_PATH = /cache/julia-buildkite-plugin/depots/7aa0085e-79a4-45f3-a5bd-9743c91cf3da

Package Information:

Status `/cache/build/default-amdci4-4/julialang/turingtutorials/tutorials/14-minituring/Project.toml`
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  [fce5fe82] Turing v0.28.1
  [9a3f8284] Random

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