Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

DNest4

MIT License

DNest4 is a C++11 implementation of Diffusive Nested Sampling, a Markov Chain Monte Carlo (MCMC) algorithm for Bayesian Inference and Statistical Mechanics. You can use it in a few different ways:

  • Implement your model in C++, compile it and have it run super fast.
  • Implement trans-dimensional models with the RJObject template class.
  • Implement your model as a Python class.
  • Write up your model in Python, using a BUGS-style approach see here to learn how (more documentation coming later for this).

Papers

There is a paper describing DNest4 installation and usage in the Journal of Statistical software. You might also want to read the original paper describing the Diffusive Nested Sampling algorithm itself. If you find this software useful in your research, please cite one or both of these papers. Here are the citations:

Brewer, B., & Foreman-Mackey, D. (2018). DNest4: Diffusive Nested Sampling in C++ and Python.
Journal of Statistical Software, 86(7), 1 - 33. doi:http://dx.doi.org/10.18637/jss.v086.i07

Brewer, B. J., Pártay, L. B., & Csányi, G. (2011). Diffusive nested sampling.
Statistics and Computing, 21(4), 649-656.

Dependencies

You will need a C++ compiler that supports the C++11 standard, along with Python 3 and the Python packages NumPy, scipy, matplotlib, Cython, and Numba. Anaconda is a suitable Python distribution to use.

Compiling

Note for Mac users:

On some Macs, g++ is an alias for clang, which is a C compiler. If this is the case for you, you'll need to edit the first line of the Makefile so that it uses clang++, which is a C++ compiler.

You can compile the DNest4 library (libdnest4) using the Makefile in the code directory using:

cd code
make

Along with building the library this will compile all the examples. Then, install the Python package.

The easiest way to do this is with:

pip install -U dnest4

but you can install the development version using:

python setup.py install

in the root directory of this repository.

Alternative build process with SCons

However, you can also compile and install the library using SCons. To do this you just need to run:

scons install

By default it will attempt to install the library in /usr/local (with the library files in /usr/local/lib and the headers in /usr/local/include/dnest4), so the above command must be run as a user with root access or using sudo. To install to a different location you can instead run:

scons install --prefix <install_location>

where <install_location> is the base path for the install.

To install with GDB enabled during the library's compilation you can add the --debug-mode flag to the install command.

Currently, the Scons installation does not compile the examples or the Python library. Any additions to this installation process are welcome.

(c) 2015--2018 Brendon J. Brewer and contributors. LICENCE: MIT. See the LICENSE file for details.

This work was supported by a Marsden Fast Start grant from the Royal Society of New Zealand.

You can’t perform that action at this time.