Simple example of using NUTS with the new iterator interface in AbstractMCMC.jl available using Turing.jl > 0.15.

turing-new-interface.jl

julia> using Turing, Random

julia> @model function gdemo(xs)
           # Assumptions
           σ² ~ InverseGamma(2, 3)
           μ ~ Normal(0, √σ²)
           # Observations
           for i = 1:length(xs)
               xs[i] ~ Normal(μ, √σ²)
           end
       end

gdemo (generic function with 2 methods)

julia> # Set up.
       xs = randn(100);

julia> model = gdemo(xs);

julia> # Sampler.
       alg = NUTS(0.65);

julia> kwargs = (nadapts=50,);

julia> num_samples = 100;

julia> ### The following two methods are equivalent ###
       ## Using `sample` ##
       rng = MersenneTwister(42);

julia> chain = sample(rng, model, alg, num_samples; kwargs...)
┌ Info: Found initial step size
└   ϵ = 0.4
Sampling 100%|█████████████████████████████████████████████████████| Time: 0:00:00
Chains MCMC chain (100×14×1 Array{Float64, 3}):

Iterations        = 51:1:150
Number of chains  = 1
Samples per chain = 100
Wall duration     = 1.12 seconds
Compute duration  = 1.12 seconds
parameters        = σ², μ
internals         = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, max_hamiltonian_energy_error, tree_depth, numerical_error, step_size, nom_step_size

Summary Statistics
  parameters      mean       std      mcse   ess_bulk   ess_tail      rhat   ess_per_sec 
      Symbol   Float64   Float64   Float64    Float64    Float64   Float64       Float64 

          σ²    1.1090    0.1728    0.0591    10.8467    51.5563    1.1136        9.6587
           μ   -0.1753    0.1030    0.0126    66.7404    78.3393    0.9940       59.4304

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

          σ²    0.8499    0.9980    1.1052    1.2155    1.4554
           μ   -0.3526   -0.2485   -0.1774   -0.1074    0.0239


julia> ## Using the iterator-interface ##
       rng = MersenneTwister(42);

julia> spl = DynamicPPL.Sampler(alg);

julia> nadapts = 50;

julia> # Create an iterator we can just step through.
       it = AbstractMCMC.Stepper(rng, model, spl, kwargs);

julia> # Initial sample and state.
       transition, state = iterate(it);
┌ Info: Found initial step size
└   ϵ = 0.4

julia> # Simple container to hold the samples.
       transitions = [];

julia> # Simple condition that says we only want `num_samples` samples.
       condition(spls) = length(spls) < num_samples
condition (generic function with 1 method)

julia> # Sample until `condition` is no longer satisfied
       while condition(transitions)
           # For an iterator we pass in the previous `state` as the second argument
           transition, state = iterate(it, state)
           # Save `transition` if we're not adapting anymore
           if state.i > nadapts
               push!(transitions, transition)
           end
       end

julia> length(transitions), state.i, state.i == length(transitions) + nadapts
(100, 150, true)

julia> # Finally, if you want to convert the vector of `transitions` into a
       # `MCMCChains.Chains` like is typically done:
       chain = AbstractMCMC.bundle_samples(
           map(identity, transitions),  # trick to concretize the eltype of `transitions`
           model,
           spl,
           state,
           MCMCChains.Chains
       )
Chains MCMC chain (100×14×1 Array{Float64, 3}):

Iterations        = 1:1:100
Number of chains  = 1
Samples per chain = 100
parameters        = σ², μ
internals         = lp, n_steps, is_accept, acceptance_rate, log_density, hamiltonian_energy, hamiltonian_energy_error, max_hamiltonian_energy_error, tree_depth, numerical_error, step_size, nom_step_size

Summary Statistics
  parameters      mean       std      mcse   ess_bulk   ess_tail      rhat   ess_per_sec 
      Symbol   Float64   Float64   Float64    Float64    Float64   Float64       Missing 

          σ²    1.1090    0.1728    0.0591    10.8467    51.5563    1.1136       missing
           μ   -0.1753    0.1030    0.0126    66.7404    78.3393    0.9940       missing

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

          σ²    0.8499    0.9980    1.1052    1.2155    1.4554
           μ   -0.3526   -0.2485   -0.1774   -0.1074    0.0239