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A C++ library of Markov Chain Monte Carlo (MCMC) methods

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kthohr/mcmc

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MCMCLib is a lightweight C++ library of Markov Chain Monte Carlo (MCMC) methods.

Features:

  • A C++11/14/17 library of well-known MCMC algorithms.
  • Parallelized samplers designed for multi-modal distributions, including:
    • Adaptive Equi-Energy Sampler (AEES)
    • Differential Evolution (DE)
  • For fast and efficient matrix-based computation, MCMCLib supports the following templated linear algebra libraries:
  • Automatic differentiation functionality is available through use of theAutodiff library
  • OpenMP-accelerated algorithms for parallel computation.
  • Straightforward linking with parallelized BLAS libraries, such asOpenBLAS.
  • Available as a single precision (float) or double precision (double) library.
  • Available as a header-only library, or as a compiled shared library.
  • Released under a permissive, non-GPL license.

Contents:

Algorithms

A list of currently available algorithms includes:

  • Adaptive Equi-Energy Sampler (AEES)
  • Differential Evolution (DE-MCMC)
  • Hamiltonian Monte Carlo (HMC)
  • Metropolis-adjusted Langevin algorithm (MALA)
  • No-U-Turn Sampler (NUTS)
  • Random Walk Metropolis-Hastings (RWMH)
  • Riemannian Manifold Hamiltonian Monte Carlo (RM-HMC)

Documentation

Full documentation is available online:

Documentation Status

A PDF version of the documentation is availablehere.

API

The MCMCLib API follows a relatively simple convention, with most algorithms called in the following manner:

algorithm_id(<initial values>, <log posterior kernel function of the target distribution>, <storage for posterior draws>, <additional data for the log posterior kernel function>);

The inputs, in order, are:

  • A vector of initial values used to define the starting point of the algorithm.
  • A user-specified function that returns the log posterior kernel value of the target distribution.
  • An array to store the posterior draws.
  • The final input is optional: it is any object that contains additional data necessary to evaluate the log posterior kernel function.

For example, the RWMH algorithm is called using:

boolrwmh(const ColVec_t& initial_vals, std::function<fp_t (const ColVec_t& vals_inp,void* target_data)> target_log_kernel, Mat_t& draws_out, void* target_data);

whereColVec_t is used to represent, e.g.,arma::vec orEigen::VectorXd types.

Installation

MCMCLib is available as a compiled shared library, or as header-only library, for Unix-alike systems only (e.g., popular Linux-based distros, as well as macOS). Use of this library with Windows-based systems, with or without MSVC,is not supported.

Requirements

MCMCLib requires either the Armadillo or Eigen C++ linear algebra libraries. (Note that Eigen version 3.4.0 requires a C++14-compatible compiler.)

Before including the header files, defineone of the following:

#defineMCMC_ENABLE_ARMA_WRAPPERS#defineMCMC_ENABLE_EIGEN_WRAPPERS

Example:

#defineMCMC_ENABLE_EIGEN_WRAPPERS#include"mcmc.hpp"

Installation Method 1: Shared Library

The library can be installed on Unix-alike systems via the standard./configure && make method.

First clone the library and any necessary submodules:

# clone mcmc into the current directorygit clone https://github.com/kthohr/mcmc ./mcmc# change directorycd ./mcmc# clone necessary submodulesgit submodule update --init

Set (one) of the following environment variablesbefore runningconfigure:

export ARMA_INCLUDE_PATH=/path/to/armadilloexport EIGEN_INCLUDE_PATH=/path/to/eigen

Finally:

# build and install with Eigen./configure -i"/usr/local" -l eigen -pmakemake install

The final command will install MCMCLib into/usr/local.

Configuration options (see./configure -h):

     Primary

  • -h print help
  • -i installation path; default: the build directory
  • -f floating-point precision mode; default:double
  • -l specify the choice of linear algebra library; choosearma oreigen
  • -m specify the BLAS and Lapack libraries to link with; for example,-m "-lopenblas" or-m "-framework Accelerate"
  • -o compiler optimization options; defaults to-O3 -march=native -ffp-contract=fast -flto -DARMA_NO_DEBUG
  • -p enable OpenMP parallelization features (recommended)

     Secondary

  • -c a coverage build (used with Codecov)
  • -d a 'development' build
  • -g a debugging build (optimization flags set to-O0 -g)

     Special

  • --header-only-version generate a header-only version of MCMCLib (seebelow)

Installation Method 2: Header-only Library

MCMCLib is also available as a header-only library (i.e., without the need to compile a shared library). Simply runconfigure with the--header-only-version option:

./configure --header-only-version

This will create a new directory,header_only_version, containing a copy of MCMCLib, modified to work on an inline basis. With this header-only version, simply include the header files (#include "mcmc.hpp) and set the include path to thehead_only_version directory (e.g.,-I/path/to/mcmclib/header_only_version).

R Compatibility

To use MCMCLib with an R package, first generate a header-only version of the library (seeabove). Then simply add a compiler definition before including the MCMCLib files.

  • For RcppArmadillo:
#defineMCMC_USE_RCPP_ARMADILLO#include"mcmc.hpp"

At this time, builds usingRcppEigen are not supported as MCMCLib requires a version of Eigen >= v3.4.0.

Example

To illustrate MCMCLib at work, consider the problem of sampling values of the mean parameter of a normal distribution.

Code:

#defineMCMC_ENABLE_EIGEN_WRAPPERS#include"mcmc.hpp"inlineEigen::VectorXdeigen_randn_colvec(size_t nr){static std::mt19937 gen{ std::random_device{}() };static std::normal_distribution<> dist;return Eigen::VectorXd{ nr }.unaryExpr([&](double x) { (void)(x);returndist(gen); });}structnorm_data_t {double sigma;    Eigen::VectorXd x;double mu_0;double sigma_0;};doublell_dens(const Eigen::VectorXd& vals_inp,void* ll_data){constdouble pi =3.14159265358979;//constdouble mu =vals_inp(0);norm_data_t* dta =reinterpret_cast<norm_data_t*>(ll_data);constdouble sigma = dta->sigma;const Eigen::VectorXd x = dta->x;constint n_vals = x.size();//constdouble ret = - n_vals * (0.5 *std::log(2*pi) +std::log(sigma)) - (x.array() - mu).pow(2).sum() / (2*sigma*sigma);//return ret;}doublelog_pr_dens(const Eigen::VectorXd& vals_inp,void* ll_data){constdouble pi =3.14159265358979;//norm_data_t* dta =reinterpret_cast<norm_data_t* >(ll_data);constdouble mu_0 = dta->mu_0;constdouble sigma_0 = dta->sigma_0;constdouble x =vals_inp(0);constdouble ret = -0.5*std::log(2*pi) -std::log(sigma_0) -std::pow(x - mu_0,2) / (2*sigma_0*sigma_0);return ret;}doublelog_target_dens(const Eigen::VectorXd& vals_inp,void* ll_data){returnll_dens(vals_inp,ll_data) +log_pr_dens(vals_inp,ll_data);}intmain(){constint n_data =100;constdouble mu =2.0;norm_data_t dta;    dta.sigma =1.0;    dta.mu_0 =1.0;    dta.sigma_0 =2.0;     Eigen::VectorXd x_dta = mu +eigen_randn_colvec(n_data).array();    dta.x = x_dta;     Eigen::VectorXdinitial_val(1);initial_val(0) =1.0;//    mcmc::algo_settings_t settings;    settings.rwmh_settings.par_scale =0.4;    settings.rwmh_settings.n_burnin_draws =2000;    settings.rwmh_settings.n_keep_draws =2000;//    Eigen::MatrixXd draws_out;mcmc::rwmh(initial_val, log_target_dens, draws_out, &dta, settings);//      std::cout <<"rwmh mean:\n" << draws_out.colwise().mean() << std::endl;    std::cout <<"acceptance rate:" <<static_cast<double>(settings.rwmh_settings.n_accept_draws) / settings.rwmh_settings.n_keep_draws << std::endl;//return0;}

On x86-based computers, this example can be compiled using:

g++ -Wall -std=c++14 -O3 -mcpu=native -ffp-contract=fast -I$EIGEN_INCLUDE_PATH -I./../../include/ rwmh_normal_mean.cpp -o rwmh_normal_mean.out -L./../.. -lmcmc

Check the/examples directory for additional examples, andhttps://mcmclib.readthedocs.io/en/latest/ for a detailed description of each algorithm.

Automatic Differentiation

By combining Eigen with theAutodiff library, MCMCLib provides experimental support for automatic differentiation.

The example below uses forward-mode automatic differentiation to compute the gradient of the Gaussian likelihood function, and the HMC algorithm to sample from the posterior distribution of the mean and variance parameters.

#defineMCMC_ENABLE_EIGEN_WRAPPERS#include"mcmc.hpp"#include<autodiff/forward/real.hpp>#include<autodiff/forward/real/eigen.hpp>inlineEigen::VectorXdeigen_randn_colvec(size_t nr){static std::mt19937 gen{ std::random_device{}() };static std::normal_distribution<> dist;return Eigen::VectorXd{ nr }.unaryExpr([&](double x) { (void)(x);returndist(gen); });}structnorm_data_t {    Eigen::VectorXd x;};doublell_dens(const Eigen::VectorXd& vals_inp, Eigen::VectorXd* grad_out,void* ll_data){constdouble pi =3.14159265358979;norm_data_t* dta =reinterpret_cast<norm_data_t*>(ll_data);const Eigen::VectorXd x = dta->x;//    autodiff::real u;    autodiff::ArrayXreal xd = vals_inp.eval();    std::function<autodiff::real (const autodiff::ArrayXreal& vals_inp)> normal_dens_log_form \    = [x, pi](const autodiff::ArrayXreal& vals_inp) -> autodiff::real    {        autodiff::real mu    =vals_inp(0);        autodiff::real sigma =vals_inp(1);return - x.size() * (0.5 *std::log(2*pi) +autodiff::detail::log(sigma)) - (x.array() - mu).pow(2).sum() / (2*sigma*sigma);    };//if (grad_out) {        Eigen::VectorXd grad_tmp =autodiff::gradient(normal_dens_log_form,autodiff::wrt(xd),autodiff::at(xd), u);        *grad_out = grad_tmp;    }else {        u =normal_dens_log_form(xd);    }//return u.val();}doublelog_target_dens(const Eigen::VectorXd& vals_inp, Eigen::VectorXd* grad_out,void* ll_data){returnll_dens(vals_inp,grad_out,ll_data);}intmain(){constint n_data =1000;constdouble mu =2.0;constdouble sigma =2.0;norm_data_t dta;      Eigen::VectorXd x_dta = mu + sigma *eigen_randn_colvec(n_data).array();    dta.x = x_dta;      Eigen::VectorXdinitial_val(2);initial_val(0) = mu +1;// muinitial_val(1) = sigma +1;// sigma      mcmc::algo_settings_t settings;      settings.hmc_settings.step_size =0.08;    settings.hmc_settings.n_burnin_draws =2000;    settings.hmc_settings.n_keep_draws =2000;//      Eigen::MatrixXd draws_out;mcmc::hmc(initial_val, log_target_dens, draws_out, &dta, settings);//      std::cout <<"hmc mean:\n" << draws_out.colwise().mean() << std::endl;    std::cout <<"acceptance rate:" <<static_cast<double>(settings.hmc_settings.n_accept_draws) / settings.hmc_settings.n_keep_draws << std::endl;//return0;}

Compile with:

g++ -Wall -std=c++17 -O3 -march=native -ffp-contract=fast -I/path/to/eigen -I/path/to/autodiff -I/path/to/mcmc/include hmc_normal_autodiff.cpp -o hmc_normal_autodiff.cpp -L/path/to/mcmc/lib -lmcmc

See thedocumentation for more details on this topic.

Author

Keith O'Hara

License

Apache Version 2

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