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## Ensemble MCMC sampler

version 1.8.0.0 (372 KB) by
An affine invariant ensemble Markov Chain Monte Carlo sampler

Updated 22 Jun 2018

From GitHub

Cascaded affine invariant ensemble MCMC sampler. "The MCMC hammer"

gwmcmc is an implementation of the Goodman and Weare 2010 Affine
invariant ensemble Markov Chain Monte Carlo (MCMC) sampler. MCMC sampling
enables bayesian inference. The problem with many traditional MCMC samplers
is that they can have slow convergence for badly scaled problems, and that
it is difficult to optimize the random walk for high-dimensional problems.
This is where the GW-algorithm really excels as it is affine invariant. It
can achieve much better convergence on badly scaled problems. It is much
simpler to get to work straight out of the box, and for that reason it
truly deserves to be called the MCMC hammer.

(This code uses a cascaded variant of the Goodman and Weare algorithm).

USAGE:
[models,logP]=gwmcmc(minit,logPfuns,mccount,[Parameter,Value,Parameter,Value]);

INPUTS:
minit: an MxW matrix of initial values for each of the walkers in the
ensemble. (M:number of model params. W: number of walkers). W
should be atleast 2xM. (see e.g. mvnrnd).
logPfuns: a cell of function handles returning the log probality of a
proposed set of model parameters. Typically this cell will
contain two function handles: one to the logprior and another
to the loglikelihood. E.g. {@(m)logprior(m) @(m)loglike(m)}
mccount: What is the desired total number of monte carlo proposals.
This is the total number, -NOT the number per chain.

Named Parameter-Value pairs:
'StepSize': unit-less stepsize (default=2.5).
'ThinChain': Thin all the chains by only storing every N'th step (default=10)
'ProgressBar': Show a text progress bar (default=true)
'Parallel': Run in ensemble of walkers in parallel. (default=false)

OUTPUTS:
models: A MxWxT matrix with the thinned markov chains (with T samples
per walker). T=~mccount/p.ThinChain/W.
logP: A PxWxT matrix of log probabilities for each model in the
models. here P is the number of functions in logPfuns.

Note on cascaded evaluation of log probabilities:
The logPfuns-argument can be specifed as a cell-array to allow a cascaded
evaluation of the probabilities. The computationally cheapest function should be
placed first in the cell (this will typically the prior). This allows the
routine to avoid calculating the likelihood, if the proposed model can be
rejected based on the prior alone.
logPfuns={logprior loglike} is faster but equivalent to
logPfuns={@(m)logprior(m)+loglike(m)}

TIP: if you aim to analyze the entire set of ensemble members as a single
sample from the distribution then you may collapse output models-matrix
thus: models=models(:,:); This will reshape the MxWxT matrix into a
Mx(W*T)-matrix while preserving the order.

EXAMPLE: Here we sample a multivariate normal distribution.

%define problem:
mu = [5;-3;6];
C = [.5 -.4 0;-.4 .5 0; 0 0 1];
iC=pinv(C);
logPfuns={@(m)-0.5*sum((m-mu)'*iC*(m-mu))}

%make a set of starting points for the entire ensemble of walkers
minit=randn(length(mu),length(mu)*2);

%Apply the MCMC hammer
[models,logP]=gwmcmc(minit,logPfuns,100000);
models(:,:,1:floor(size(models,3)*.2))=[]; %remove 20% as burn-in
models=models(:,:)'; %reshape matrix to collapse the ensemble member dimension
scatter(models(:,1),models(:,2))
prctile(models,[5 50 95])

References:
Goodman & Weare (2010), Ensemble Samplers With Affine Invariance, Comm. App. Math. Comp. Sci., Vol. 5, No. 1, 65–80
Foreman-Mackey, Hogg, Lang, Goodman (2013), emcee: The MCMC Hammer, arXiv:1202.3665

WebPage: https://github.com/grinsted/gwmcmc

-Aslak Grinsted 2015

### Cite As

Aslak Grinsted (2021). Ensemble MCMC sampler (https://github.com/grinsted/gwmcmc), GitHub. Retrieved .

Igor Vaiman

I have a serious suspicion that this very good-looking sampler is incorrect with its cascaded implementation. I believe the same consideration is referenced in the previous comment.

I have created issue on GitHub with more detailed description of what I believe is wrong here: https://github.com/grinsted/gwmcmc/issues/8

I hope to hear back from authors of this code (if it's still mainteined) and fix the problem. Meanwhile you can try using (and by all means compare with the original!) my own derivation from the sampler, with this issue improved: https://github.com/nj-vs-vh/gwmcmc2

Very helpful. Just wondering, why it does not give the information of acceptance fraction at the end of simulations?

Darcy Cordell

This is a great script. However, can someone elaborate on the line that reads: "lr(1)<(numel(proposedm(:,wix))-1)*log(zz(wix))". I find that the rejection rate is quite high (even for small step size) and it seems to be related to this. I looked in Goodman and Weare (2010) and couldn't find anything about the number of elements in the model and how that is relevant to the accept/reject criterion. Apologies if this is a simple question...

Liwei Cheng

Jacob Thompson

Whenever I try to implement this function the paramater guesses do not change from the initial values. I suspect that it may have to do with how I declared logPfuns?

Sanjay Manohar

Sanjay Manohar

Note- the parallel implementation generates one sample in each worker, then needs to coalesce before running the next sample. So only useful with very slow objective functions... Needs to be improved using spmd? if anyone knows how to do this that would be great!

name1 name2

hi what is the difference between your algorithm and the rjmcmc of green?

Can I get 95% confidence intervals for each parameter ?

Aslak Grinsted

The algorithm can only explore the dimensions of space spanned by the starting points of the walkers. If all the walkers start in the same point then they will never move.

Romain Fiévet

works great otherwise, nice good!

Romain Fiévet

I am having a problem describing a simple Gaussian prior with this code. The chain only has constant value! I tried the line_fit example and it works, but the moment I remove the randomness from the initial ensemble of walkers, it also contains only constant chains. Is this normal behavior ? Shouldnt the random walk move away from the initial position ?

Kusa Amu

Is it possible to apply hierarchical bayes? If yes, could you provide an example to determine hyper-parameters of the slope from the linefit example?

Liguang Jiang

No boundary handing when new candidate is proposed!

AMRANE youcef

can help me

Nathan Bowman

Aslak Grinsted

@Yalei: Good question. I guess this website shows uncompressed size. But it is out of my control.

Yalei Ding

File Size: 1.35 MB
File ID: #49820
Version: 1.8

Aslak Grinsted

@Arpad Rozsas: thank you. I have fixed the typo in the example

Thanks for this great code!
I think there is a minor error in the example.
logP={@(m)-0.5*sum((m-mu)'*iC*(m-mu))}

should be:
logPfuns={@(m)-0.5*sum((m-mu)'*iC*(m-mu))}

Luis O'Shea

##### MATLAB Release Compatibility
Created with R2014a
Compatible with any release
##### Platform Compatibility
Windows macOS Linux