The Turing.jl package provides a great interface for performing Bayesian inference using a variety of algorithms. But what algorithm allows you to go from data to samples the quickest? In this blog post I will be sampling from a simple model to demonstrate how quickly you can sample from a model. This is useful for anyone that has a Turing model in the middle of another model that they need to get samples from.

Toy Model

For the toy model we will be sampling from the Beta distribution.

using Distributions

testData = rand(Beta(3, 4), 100);

Writing the model in Julia is simple enough. We will be using an inverse Gamma prior for the free parameters.

using Turing

@model betaSample(y) = begin
    alpha ~ InverseGamma(2, 1/8)
    beta ~ InverseGamma(2, 1/8)
    for i in eachindex(y)
        y[i] ~ Beta(alpha, beta)
sample(betaSample(testData), MH(250))
[MH] Finished with
  Running time        = 0.6196572280000009;
  Accept rate         = 0.028;

Object of type Chains, with data of type 250×4×1 Array{Union{Missing, Float64},3}

Log evidence      = 0.0
Iterations        = 1:250
Thinning interval = 1
Chains            = 1
Samples per chain = 250
internals         = elapsed, lp
parameters        = alpha, beta

       Mean    SD   Naive SE  MCSE    ESS 
alpha 0.4483 0.1063   0.0067 0.0453 5.4955
 beta 0.2394 0.0461   0.0029 0.0191 5.8285

This quick test verifies that I’ve written the model correctly and everything can be sampled.

Available Samplers

In this blog post I am interested in being able to quickly sample from the posterior distribution and extract some sensible parameter samples. I will be assessing 4 different samplers.

  • Hamiltonian Monte Carlo (HMC)
  • Metropolis Hastings (MH)
  • No U Turn Sampling (NUTS)
  • Stochastic Gradient Langevin Dynamics (SGLD)

Each have their own way of sampling from the posterior distribution, with benefits and drawbacks. Turing provides an standard interface to use these algorithms without having to worry about the fine details.

We want to be able to ‘set and forget’ the parameters of the sampler so will be using the defaults given at If the sampling fails, I will tweak the parameters until it works.

I’ve chosen these 4 samplers out of familiarity. HMC and NUTS are the algorithms used in Stan. Metropolis Hastings is the one sampler everyone has implemented themselves at one point and SGLD is an improved version of that. I’m not including the particle samplers, mainly because I’m unfamiliar with their use cases.

We will be running the samplers for 1000 iterations and benchmarking for 120 seconds. This should give us enough trials to calculate an average running time of the samplers.

using BenchmarkTools
BenchmarkTools.DEFAULT_PARAMETERS.seconds = 120.0

numIts = 1000


hmcSamps = sample(betaSample(testData), HMC(numIts, 0.01, 10));
hmcRunTime = @benchmark sample(betaSample($testData), HMC($numIts, 0.01, 10));

Metropolis Hastings

mhSamps = sample(betaSample(testData), MH(numIts));
mhRunTime = @benchmark sample(betaSample($testData), MH($numIts));


nutsSamps = sample(betaSample(testData), NUTS(numIts, 0.65));
nutsRunTime = @benchmark sample(betaSample($testData), NUTS($numIts, 0.65));


sgldSamps = sample(betaSample(testData), SGLD(numIts, 0.01));
sgldRunTime = @benchmark sample(betaSample($testData), SGLD($numIts, 0.01));


using Plots
function extractMeanTime(runtime)
    median(runtime).time /1e9

nms = ["SGLD", "HMC", "MH", "NUTS"]
runTimes = [sgldRunTime, hmcRunTime, mhRunTime, nutsRunTime]

times = map(extractMeanTime, runTimes)

map(length, runTimes)
4-element Array{Int64,1}:

Here we can see that each sampler has been evaluated a number of times. The median running time is extracted and converted into seconds.

bar(nms, times, ylabel="Average Time (seconds)", legend=false)


HMC and NUTS are the slowest. SGLD and MH performing the quickest which is the expected result. The calculations involved in the HMC and NUTS algorithms are a bit more complex.

However, it is not always about speed. We want to make sure that the sampler is moving towards the correct parameters and not just moving about randomly. We need to a check the quality of the samples. To assess this we want to check the Effective Number of Samples which is a metric that discounts the samples by the autocorrelation between values. Essentially, a better sampling algorithm will produce a higher number of effective samples for the same number of iterations.

Therefore, instead of just looking at the running time, we want to divide the effective sample size by the running time to produce a Effective Samples per Second value.

function extractMeanandESS(smps)
    params = MCMCChains.summarystats(smps)
    alphaESS = params.summaries[1].value[1, 5,1]
    betaESS = params.summaries[1].value[2, 5,1]
    alphaMean = params.summaries[1].value[1, 1,1]
    betaMean = params.summaries[1].value[2, 1,1]
    [alphaMean, betaMean, alphaESS, betaESS]

allSamps = [sgldSamps, hmcSamps, mhSamps, nutsSamps]
params = map(extractMeanandESS, allSamps)
paramSummaries = reduce(hcat, params)'
4×4 LinearAlgebra.Adjoint{Float64,Array{Float64,2}}:
 2.6097  3.1185   22.4958   21.1824
 2.8535  3.3886  424.781   458.39  
 0.3993  0.7389   11.0827    9.3238
 2.9507  3.5326   51.6685   39.9213
using StatsPlots
alphaESSperSecond = paramSummaries[:,3] ./ times
betaESSperSecond = paramSummaries[:,4] ./ times

groupedbar(nms, hcat(alphaESSperSecond, betaESSperSecond), label=["Alpha", "Beta"], ylabel="Effective Samples per Second")


So NUTS produces the least amount of effective samples per second run. Which is surprising, but for such a simple model it doesn’t cause too much of a concern. I would predict that as the model increased in complexity, the ESS would improve compared to the other samplers.

From this graph, we are inclined to think that either HMC or SGLD are the preferable sampling algorithms.

Parameter Results

pdfs = map(x-> pdf.(Beta(x[1], x[2]), collect(0:0.01:1)), params)

histogram(testData, normed=true, label="Training Data", fillalpha=0.4)

plot!(collect(0:0.01:1), pdfs[1], label=nms[1], linewidth=2)
plot!(collect(0:0.01:1), pdfs[2], label=nms[2], linewidth=2)
plot!(collect(0:0.01:1), pdfs[3], label=nms[3], linewidth=2)
plot!(collect(0:0.01:1), pdfs[4], label=nms[4], linewidth=2)
plot!(collect(0:0.01:1), pdf.(Beta(3,4), collect(0:0.01:1)), label="True", linewidth=2)


Here we can see that the Metropolis Hastings sampler is nowhere near the true distribution. All the others have done well and are close to the true distribution. Given that they only have 100 datapoints to go by and we are just taking the mean of the samples its not a bad result.


So in conclusion it looks like we would be inclined to use the HMC sampler. It produces the best ESS per second values and doesn’t require too much tinkering with. If speed is an absolute priority, then SGLD might be more appropriate, its 6 times faster at the cost of about 100 effective samples a second.

Definitely do not use Metropolis Hastings though. Out of the box it hasn’t even got close to the correct value. I’m probably missing setting some parameter.

There are a number of weaknesses in this analysis. Firstly, we have not considered multiple chains to check for convergence. Currently, multiple chains in parallel are not supported out of the box for Turing.jl so rather than faff about using multiple chains by hand, I’ve just stuck to the one. Secondly, BenchmarkTools runs for a fixed amount of time rather than a fixed amount of samples. I’ve changed the parameters of the benchmark to try and account for this, but it still isn’t exact. Finally, there are still the particle samplers. I’ve shied away from them, mainly because I’ve never used them before and not 100% on their use case.