npb.tar

        Nonparametric Bayesian estimation subject to overidentified 
        moment equations is a challenge because the support of the 
        posterior is a manifold of lower dimension than the number of 
        model parameters. The manifold therefore has Lebesgue measure 
        zero thus inhibiting the use of the most commonly used Bayesian 
        estimation method: MCMC (Markov Chain Monte Carlo).  NPB solves 
        this problem by adapting the MCMC Surface Sampling Algorithm of

           Zappa, Emilio, Miranda Holmes-Cerfon,and Jonathan Goodman 
           (2018), "Monte Carlo on Manifolds: Sampling Densities and 
           Integrating Functions," Communications on Pure and Applied 
           Mathematics 71, 2609--2647.

        The SNP density is used as the likelihood.  The method is 
        nonparametric because the SNP density is a sieve; that is, a 
        series expansion of a density that is dense in a relevant norm.

The package is written in C++.  It includes a user's guide, examples,
and necessary libraries.

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either version 2 of
the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public
License along with this program; if not, write to the Free
Software Foundation, Inc., 51 Franklin Street, Fifth Floor,
Boston, MA 02110-1301 USA.