https://github.com/quatrope/automix
A refactoring of David Hastie's AutoMix Reversible Jump MCMC
https://github.com/quatrope/automix
automix mcmc mcmc-sampler reversible-jump-mcmc sampler
Last synced: 4 months ago
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A refactoring of David Hastie's AutoMix Reversible Jump MCMC
- Host: GitHub
- URL: https://github.com/quatrope/automix
- Owner: quatrope
- License: other
- Created: 2019-12-16T19:08:46.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-10-31T13:44:17.000Z (over 4 years ago)
- Last Synced: 2024-12-30T12:26:35.581Z (6 months ago)
- Topics: automix, mcmc, mcmc-sampler, reversible-jump-mcmc, sampler
- Language: Objective-C
- Homepage:
- Size: 2.51 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
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# AutoMix
## The AutoMix Sampler
_This README is a modified version of the original README.txt file._
_Check the original README.txt for license information._
The AutoMix package is a C program for Unix-like systems, implementing the automatic Reversible Jump MCMC sampler of the same name described in Chapters 4, 5, and 6 of David Hastie's Ph.D. thesis (included in `docs/thesis`).
While the original AutoMix is highly useful, the fact that it can only be used as an executable can limit its applicability.
**LibAutoMix makes the core algorithms of the AutoMix program available as a library of stand-alone functions and data structures.**
The code is completely derived from the original AutoMix code base and aims to produce results compatible with it whenever possible.
LibAutoMix is a C library with no dependencies outside the standard library.
### Warning:
> Potential users should carefully understand the limitations of using the AutoMix sampler.
> The reliability of results from this sampler depends on many factors,
> including the scaling of the parameters and the degree of multimodality of the within-model conditionals of the target distribution.
## What is Reversible Jump MCMC?
Reversible Jump Markov Chain Monte Carlo (RJMCMC) extends the standard MCMC sampler to include a
discrete random variable `k` that represents the index of a *model*.
So, instead of sampling from the usual parameter space of a given distribution,
RJMCMC will also sample across different models (distributions).
The final samples of a RJMCMC reflect the probabilities of the parameters of each model,
but also the relative probabilities of the models themselves.
## Main advantages of AutoMix:
* Reversible Jump MCMC allows sampling from several distributions (models) simultaneously.
* The different models may have different number of free parameters (dimension).
* The relative frequency of sampling for different models is proportional to the probability of the model.
That means that AutoMix can be used as a model selection sampler.
* AutoMix requires minimum input from the user.
* AutoMix automatically adapts proposal distributions with a multi-modal Normal mixture.
## Simple example program
AutoMix is most useful in the situation where multiple models are tested simultaneously,
Nevertheless, below is an example for a simple 1D Normal distribution sampler.
// main.c file
#include "automix.h"
double logp_normal_sampler(int model, double* xp);
int main() {
int nmodels = 1;
int model_dims[] = {1};
double initRWM[] = {0.5, 0.5};
amSampler am;
initAMSampler(&am, nmodels, model_dims, logp_normal_sampler, initRWM);
burn_samples(&am, 10000);
int nsweeps = 100000;
rjmcmc_samples(&am, nsweeps);
freeAMSampler(&am);
return 0;
}
double logp_normal_sampler(int model_k, double *xp) {
double x = *xp;
double prob;
double x0 = 0.5;
double sigma = 1.0;
prob = -(x - x0) * (x - x0) / (2.0 * sigma * sigma);
return prob;
}
The previous C file will output samples from a Normal distribution.
To compile:
$ make; make install
$ sudo ldconfig # only for Linux
$ cc main.c -lautomix -o normal
$ ./normal
For full documentation, examples and a quick tour of the library, check out the [documentation](https://automix.readthedocs.io/).