versatile-mcmc

A lightweight and extensible Markov Chain Monte Carlo (MCMC) library written in modern C++.

Introduction

With this project I aim at building a modular and easy to integrate C++ framework for performing Bayesian inference and mathematical optimization tasks in high-dimensional models.

While there are several MCMC algorithms available for the software environment R, similar options for the C++ language, which is still a popular choice for scientific high-performance computations, are limited.

In order to allow for a future proof and robust code base with intuitive extensibility and good integrability of the library into existing C++ projects, I'd like to meet the following design criteria:

• Limited use of 3rd party libraries for better maintainability and control of dependencies.
  • The only mandatory requirement for now are the Boost C++ libraries. Most of them are peer-reviewed, well documented and follow decent style guidelines. Especially the numeric, linear algebra and statistical utilities are extremely helpful in the start of this project.
  • Although popular among nature scientists, I have ruled out to interface with ROOT. It is a huge dependency bringing a whole data analysis ecosystem along with it. Also, its API holds a rather questionable standing in terms of style and consistency.
• Modern object-oriented C++11 style with dashes of functional programming and templating magic where justifiable.
• Thread-safety to make way for multi-core parallelization.
• Unit testing (Google Test) and code annotations (Doxygen).

Status

• Build system, unit testing and continuous integration set up.
• Numeric + logging utilities and random number generator interfaces implemented.
• Basic classes and interfaces for proposal functions and samplers declared.
• First running example for a simple Metropolis-Hastings (MH) algorithm.

Next items on my todo list

• Automatic error control / step size adjustment for the MH algorithm.
• More export / output options.
• Hamiltonian / self-adjusting sampling algorithms.
• Real-time visualization of chain evolutions might be neat:

Supported compilers and operating systems

Essentially, all C++11 compliant compilers should work. Currently the build is being tested on Travis CI using the following OS/compiler combinations:

• Ubuntu Trusty (14.04)
  • g++ 4.8.4
  • g++ 5.3.0
  • g++ 6.1.1
  • clang++ 3.5
  • clang++ 3.9
• OS X El Capitan (10.11.5)
  • Xcode (clang) 7.3
  • Xcode (clang) 8.0

Prerequisits

• Meson build system with Ninja as backend
• Boost C++ libraries (>= 1.45.0)
• Optional:
  • Doxygen
  • Intel's Threading Building Blocks
  • Google's C++ test framework Google Test

On Debian-based systems simply use apt-get:

sudo apt-get install meson ninja libboost-all-dev doxygen libtbb-dev libgtest-dev 

If meson is not provided by your package manager, you can use the python 3 installer:

pip3 install meson

Quick Installation

• Setup meson in an out-of-source build directory (e.g. a subdirectory in the source root):

  cd versatile-mcmc
  mkdir build
  cd build
  meson .. [--prefix=/install/dir] [--buildtype=(plain|debug|debugoptimized|release)]

• Use mesonconf to revise or modify your configuration:

  mesonconf

• Both meson(.py) and mesonconf(.py) offer decent command-line help if executed with the option –help.
• To compile the code, execute ninja in the build directory:

  ninja [-v]

• Install:

  ninja install

• Run unit tests:

  ninja test

• Generate source documentation (requires doxygen):

  ninja doc

Getting Started

See the example in src/vmcmc-example.cpp on how to invoke a Metropolis-Hastings sampler on a custom likelihood function and write the sampled points to text files.