https://github.com/eggplantbren/dnest5

Diffusive Nested Sampling like you've never seen it before
https://github.com/eggplantbren/dnest5

bayesian-inference markov-chain-monte-carlo nested-sampling statistics

Last synced: 24 days ago
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Diffusive Nested Sampling like you've never seen it before

• Host: GitHub
• URL: https://github.com/eggplantbren/dnest5
• Owner: eggplantbren
• License: gpl-3.0
• Created: 2020-08-12T01:48:12.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-08-26T23:17:57.000Z (4 months ago)
• Last Synced: 2024-08-27T02:01:39.378Z (4 months ago)
• Topics: bayesian-inference, markov-chain-monte-carlo, nested-sampling, statistics
• Language: C++
• Homepage:
• Size: 145 KB
• Stars: 8
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
DNest5

======

(c) 2020--2024 Brendon J. Brewer.

Please Clone Recursively

========================

`$ git clone --recursive https://github.com/eggplantbren/DNest5`

This will pull in a submodule that I'm using.

LICENCE

=======

GNU General Public License version 3.

Dependencies

============

* Boost

* Python 3 with NumPy, matplotlib, apsw

* SQLite3

* yaml-cpp

Installation

============

To compile `DNest5`, run the build script. This generates a `Makefile` and

then runs `make` to compile everything.

`$ ./build.py`

By default, this compiles the StraightLine example from

DNest4. The two executable

files created are `main`, which does the sampling, and `postprocess`, which

creates posterior samples and computes the marginal likelihood.

Running and Outputs

===================

First run `main`, then run `showresults.py` in Python. Showresults calls

`postprocess`, so you don't need to run `postprocess` on its own. It is safe

to run `showresults.py` while `main` is still running.

Compiling a Different Model

===========================

Edit `ModelType.hpp` and change the `#include` and `using` lines

to point to a different model. Then run `./build.py` again.

At this time, all example models in the `Example` directory are implemented

using inheritance from the `UniformModel` class template.

These follow the convention where the exploration is done in terms of a coordinate system that makes the prior Uniform(0, 1) for every coordinate.

The user/model writer has to write the member functions `us_to_params` to

convert from the `us` (Uniform(0, 1) parameters) to the actual parameters,

and `log_likelihood` in order to evaluate the likelihood function.

Specifying the Options

======================

The configuration file `options.yaml` plays the role of DNest4's `OPTIONS`

and its command line arguments.

Outputs

=======

All outputs are written to the `output` directory. The files are:

* `dnest5.db`: An SQLite3 database of output, similar to DNest4's `sample.txt`,

    `sample_info.txt`, and `levels.txt`, but combined into one binary file.

* `figure?.pdf`: Figures exported by `showresults.py`. Note that if you run

    `showresults.py' while the main process is running, some inconsistencies

    can appear in Figure 3 between the particles and the levels. These

    do not have any major consequences.

* `posterior.db`: An SQLite3 database of posterior samples. Does not store

    the samples themselves, but rather refers back to the appropriate rows

    in `dnest5.db`.

* `posterior.csv`: CSV file of posterior samples.

* `results.yaml`: YAML file (plain text) with marginal likelihood values and

    related things.