AdaptiveMCMC.jl

Implementation of some simple adaptive MCMC algorithms
Author mvihola
Popularity
19 Stars
Updated Last
12 Months Ago
Started In
August 2019

documentation

AdaptiveMCMC.jl

This package provides implementations of some general-purpose random-walk based adaptive MCMC algorithms, including the following:

The aim of the package is to provide a simple and modular general-purpose implementation, which may be easily used to sample from a log-target density, but also used in a variety of custom settings.

See also AdaptiveParticleMCMC.jl which uses this package with SequentialMonteCarlo.jl for adaptive particle MCMC.

Getting the package

To get the latest registered version:

using Pkg
Pkg.add("AdaptiveMCMC")

To install the latest development version:

using Pkg
Pkg.add(url="https://github.com/mvihola/AdaptiveMCMC.jl")

Quick start

# Load the package
using AdaptiveMCMC

# Define a function which returns log-density values:
log_p(x) = -.5*sum(x.^2)

# Run 10k iterations of the Adaptive Metropolis:
out = adaptive_rwm(zeros(2), log_p, 10_000; algorithm=:am)

# Calculate '95% credible intervals':
using Statistics
mapslices(x->"$(mean(x)) ± $(1.96std(x))", out.X, dims=2)

The function adaptive_rwm ('Adaptive Random walk Metropolis') is a simple implenentation which does sampling for a given log-target density with the chosen method.

Adaptive parallel tempering

The adaptive_rwm also implements tempering, which is used if an optional argument L≥2 (number of temperature levels) is supplied. Here is a simple multimodal distribution sampled with APT:

# Multimodal target of dimension d.
function multimodalTarget(d::Int, sigma2=0.1^2, sigman=sigma2)
    # The means of mixtures
    m = [2.18 5.76; 3.25 3.47; 5.41 2.65; 4.93 1.50; 8.67 9.59;
         1.70 0.50; 2.70 7.88; 1.83 0.09; 4.24 8.48; 4.59 5.60;
         4.98 3.70; 2.26 0.31; 8.41 1.68; 6.91 5.81; 1.14 2.39;
         5.54 6.86; 3.93 8.82; 6.87 5.40; 8.33 9.50; 1.69 8.11]'
    n_m = size(m,2)
    @assert d>=2 "Dimension should be >= 2"
    let m=m, n_m=size(m,2), d=d
        function log_p(x::Vector{Float64})
            l_dens = -0.5*(mapslices(sum, (m.-x[1:2]).^2, dims=1)/sigma2)
            if d>2
                l_dens .-= 0.5*mapslices(sum, x[3:d].^2, dims=1)/sigman
            end
            l_max = maximum(l_dens) # Prevent underflow by log-sum trick
            l_max + log(sum(exp.(l_dens.-l_max)))
        end
    end
end

using AdaptiveMCMC
n = 100_000; L = 2
rwm = adaptive_rwm(zeros(2), multimodalTarget(2), n; thin=10)
apt = adaptive_rwm(zeros(2), multimodalTarget(2), div(n,L); L = L, thin=10)

# Assuming you have 'Plots' installed:
using Plots
plot(scatter(rwm.X[1,:], rwm.X[2,:], title="w/o tempering", legend=:none),
scatter(apt.X[1,:], apt.X[2,:], title="w/ tempering", legend=:none), layout=(1,2))

Using with Distributions and LabelledArrays

MCMC is often useful with hierarchical models. These may be conveniently built using Distributions and LabelledArrays packages. The following example assumes these packages to be installed.

using Distributions, LabelledArrays, AdaptiveMCMC
# Define convenience log-transform for continuous univariate distributions
struct LogTransformedDistribution{Dist <: ContinuousUnivariateDistribution}
        d::Dist
end
import Distributions.logpdf
logpdf(d::LogTransformedDistribution, x) = logpdf(d.d, exp(x)) + x
import Base.log
log(d::ContinuousUnivariateDistribution) = LogTransformedDistribution(d)

# This example is modified from Turing Getting Started:
# https://turing.ml/dev/docs/using-turing/get-started
function buildModel(x=0.0, y=1.0)
    let x=x, y=y
        function(v)
            p = 0.0
            p += logpdf(log(InverseGamma(2,3)), v.log_s)
            ss = exp(.5*v.log_s)
            p += logpdf(Normal(0, ss), v.m)
            p += logpdf(Normal(v.m, ss), x)
            p += logpdf(Normal(v.m, ss), y)
            p
        end
    end
end

# Initial state vector (labelled with keys `s` and `m`)
x0 = LVector(log_s=1.0, m=0.0); log_p = buildModel(3.3, 4.14)
# Hint: If you do not have a good guess of the mode of log_p (which is
# a good initial value for MCMC), you may use optimisation:
#using Optim; o = optimize(x -> -log_p(x), x0); x0 = o.minimizer
out = adaptive_rwm(x0, log_p, 1_000_000; thin=20)
using StatsPlots # Assuming installed
corrplot(out.X', labels=[keys(x0)...])

Resuming simulation

(This is available currently only in the development version!)

Simulation can be resumed, or continued after one simulation. Here is an example:

using AdaptiveMCMC, Random
log_p(x) = -.5*sum(x.^2)
Random.seed!(12345)
# Simulate 200 iterations first:
out = adaptive_rwm(zeros(2), log_p, 200)
# Simulate 100 iterations more:
out2 = adaptive_rwm(out.X[:,end], log_p, 100; Sp=out.S, Rp=out.R, indp=200)
# This results in exactly the same output as simulating 300 samples in one go:
Random.seed!(12345)
out2_ = adaptive_rwm(zeros(2), log_p, 300)

Custom sampler

The package provides also simple building blocks which you can use within a 'custom' MCMC sampler. Here is an example:

using AdaptiveMCMC

# Sampler in R^d
function mySampler(log_p, n, x0)

    # Initialise random walk sampler state: r.x current state, r.y proposal
    r = RWMState(x0)

    # Initialise Adaptive Metropolis state (with default parameters)
    s = AdaptiveMetropolis(x0)
    # Other adaptations are: AdaptiveScalingMetropolis,
    # AdaptiveScalingWithinAdaptiveMetropolis, and RobustAdaptiveMetropolis

    X = zeros(eltype(x0), length(x0), n) # Allocate output storage
    p_x = log_p(r.x)                     # = log_p(x0); the initial log target
    for k = 1:n

        # Draw new proposal r.x -> r.y:
        draw!(r, s)

        p_y = log_p(r.y)                      # Calculate log target at proposal
        alpha = min(one(p_x), exp(p_y - p_x)) # The Metropolis acceptance probability

        if rand() <= alpha
            p_x = p_y

            # This 'accepts', or interchanges r.x <-> r.y:
            # (NB: do not do r.x = r.y; these are (pointers to) vectors!)
            accept!(r)
        end

        # Do the adaptation update:
        adapt!(s, r, alpha, k)

        X[:,k] = r.x   # Save the current sample
     end
    X
end

# Standard normal target for testing
normal_log_p(x) = -mapreduce(e->e*e, +, x)/2

# Run 1M iterations of the sampler targetting 30d standard Normal:
X = mySampler(normal_log_p, 1_000_000, zeros(30))

More documentation

See the more detailed documentation for more information regarding the implementation. The functions also have help fields, so for instance, ? adaptive_mcmc in the Julia REPL gives a brief help of that function.

How to cite

The algorithms implemented in the package are discussed in the following reference:

  • M. Vihola. Ergonomic and reliable Bayesian inference with adaptive Markov chain Monte Carlo. In Wiley StatsRef: Statistics Reference Online, Davidian, M., Kenett, R.S., Longford, N.T., Molenberghs, G., Piegorsch, W.W., and Ruggeri, F. (eds.), Article No. stat08286, 2020. doi.org/10.1002/9781118445112.stat08286

The above is also published as the following book chapter (which can also be cited):

  • M. Vihola. Bayesian inference with Adaptive Markov chain Monte Carlo. In Computational Statistics in Data Science, Piegorsch, W.W., Levine, R.A., Zhang, H.H., and Lee, T.C.M. (eds.), Chichester: John Wiley & Sons, ISBN: 978-1-119-56107-1, 2022.

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