Differential-Independence Mixture Ensemble ("DIME") MCMC sampling for matlab
This is a standalone matlab implementation of the DIME sampler proposed in DIME MCMC: A Swiss Army Knife for Bayesian Inference (Gregor Boehl, 2022, SSRN No. 4250395).
The sampler has a series of advantages over conventional samplers:
- DIME MCMC is a (very fast) gradient-free global multi-start optimizer and, at the same time, a MCMC sampler that converges to the posterior distribution. This makes any posterior mode density maximization prior to MCMC sampling superfluous.
- The DIME sampler is pretty robust for odd shaped, multimodal distributions.
- DIME MCMC is parallelizable: many chains can run in parallel, and the necessary number of draws decreases almost one-to-one with the number of chains.
- DIME proposals are generated from an endogenous and adaptive proposal distribution, thereby providing close-to-optimal proposal distributions for black box target distributions without the need for manual fine-tuning.
There is a nice set of slides on my website which explains the DIME principle.
Copy the matlab folder from this repo somwhere on your PC and add it to your matlab path:
addpath('where/ever/to/dime_mcmc_matlab/matlab')You can get a zip file containing the complete repo here.
Note that you need the statistics toolbox for matlab. Unfortunately this toolbox does not seem to be fully compatible with pkg load statistics in Octave (sorry, not my fault. But maybe you can steal the respective files from octave.)
There exist complementary implementations of DIME MCMC in Python and Julia (where you don't need expensive toolboxes).
The core functionality is included in the function dime_mcmc:
% define your density function
log_prob = ...
% define the initial ensemble
initchain = ...
% define the number of iterations
niter = ...
% off you go sampling
[chains, lprobs, prop_mean, prop_cov] = dime_mcmc(log_prob, initchain, niter)
...When using bounded priors it is recommended to use parameter transformations to maintain high acceptance rates.
Be sure the files from above are in your matlab path. Then, lets define a challenging example distribution with three separate modes:
% make it reproducible
rng('default'); rng(1);
% define distribution
m = 2;
cov_scale = 0.05;
weight = [0.33 0.1];
ndim = 35;
% define distribution
log_prob = create_dime_test_func(ndim, weight, m, cov_scale);log_prob will now return the log-PDF of a 35-dimensional Gaussian mixture.
Important: the function returning the log-density must be vectorized, i.e. able to evaluate inputs with size (:,ndim). If you want to make use of parallelization (which is one of the central advantages of ensemble MCMC), you may want to ensure that this function evaluates its vectorized input in parallel.
Next, define the initial ensemble. In a Bayesian setup, a good initial ensemble would be a sample from the prior distribution. Here, we will go for a sample from a rather flat Gaussian distribution.
initvar = 2;
nchain = ndim*5; % a sane default
initcov = eye(ndim)*initvar;
initmean = zeros(ndim, 1);Find nchain draws that have a finite likelihood:
Setting the number of parallel chains to 5*ndim is a sane default. For highly irregular distributions with several modes you should use more chains. Very simple distributions can go with less.
Now let the sampler run for 5000 iterations.
niter = 5000;
[chains, lprobs, prop_mean, prop_cov] = dime_mcmc(log_prob, initchain, niter);A struct with options can be appended as additional argument, which are documented in the function help. The setting of opt.aimh_prob is the actual default value. For less complex distributions (e.g. distributions closer to Gaussian) a higher value can be chosen, which accelerates burn-in. The information in the progress bar has the structure [ll/MAF: <maximum log-prob>(<standard deviation of log-prob>)/<mean acceptance fraction>].
Let's plot the marginal distribution along the first dimension (remember that this actually is a 35-dimensional distribution).
% get sample and analytical marginal pdf
x = linspace(-4,4,1000);
mpdf = dime_test_func_marginal_pdf(x, cov_scale, m, weight);
sample = reshape(chains(end-fix(niter/3):end,:,1), [], 1);
% calculate a histogram with densities
bins = linspace(-3,3,50);
counts = histc(sample, bins);
density = counts / (sum(counts) * (bins(2)-bins(1)));
scale = sqrt(prop_cov(1,1)*10/8);
% plot
figure;
hold on
bar(bins + (bins(2)-bins(1))/2, density)
plot(x, mpdf)
plot(x, normpdf(x, 0, sqrt(initvar)))
plot(x, tpdf((x - prop_mean(1))/scale, 10)/scale)
xlim([-4 4])
legend({'Sample', 'Target','Initialization','Final Proposal'},'Location','northwest')
hold off
The plot is actually taken from the Python implementation because it looks soo nice. To ensure proper mixing, let us also have a look at the MCMC traces, again focussing on the first dimension:
figure;
lines = plot(chains(:,:,1),'-b');
for i = 1:length(lines)
lines(i).Color(4) = 0.05;
end
Note how chains are also switching between the three modes because of the global proposal kernel.
While DIME is a MCMC sampler, it can straightforwardly be used as a global optimization routine. To this end, specify some broad starting region (in a non-Bayesian setup there is no prior) and let the sampler run for an extended number of iterations. Finally, assess whether the maximum value per ensemble did not change much in the last few hundred iterations. In a normal Bayesian setup, plotting the associated log-likelihood over time also helps to assess convergence to the posterior distribution.
figure;
lines = plot(lprobs, '-b');
for i = 1:length(lines)
lines(i).Color(4) = 0.05;
end
If you are using this software in your research, please cite
@techreport{boehl2022mcmc,
author={Gregor Boehl},
title={Ensemble MCMC Sampling for Robust Bayesian Inference},
journal={Available at SSRN 4250395},
year={2022}
}