===============================
References and Acknowledgements
===============================

**The release paper describing the code corresponding to dynesty 1.0 can be found**
`here <https://github.com/joshspeagle/dynesty/tree/master/paper/dynesty.pdf>`_.
Unless you use the 1.0 version, you should **also** cite dynesty code used
through `zenodo <https://doi.org/10.5281/zenodo.3348367>`_


A list of papers that you should cite can always be generated directly
from the `sampler` object by calling::

    print(sampler.citations)

This will return a list of relevant papers and corresponding links to download
citation information such as BibTex files.

This list will by default include the following papers:

* Code:
  `Speagle (2020) <https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.3132S/abstract>`_
  and `Koposov et al. (2025) <https://doi.org/10.5281/zenodo.3348367>`_
  
* Nested Sampling:
  `Skilling (2004) <http://ui.adsabs.harvard.edu/abs/2004AIPC..735..395S>`_
  and `Skilling (2006) <https://projecteuclid.org/euclid.ba/1340370944>`_.

If you use the Dynamic Nested Sampling functionality
(via `DynamicNestedSampler`), this will also include:

* Dynamic Nested Sampling:
  `Higson et al. (2019)
  <https://doi.org/10.1007/s11222-018-9844-0>`_.

Depending on your specific bounding and sampling options, this may also include
the following papers:

* Single ellipsoid bound:
  `Mukherjee, Parkinson & Liddle (2006)
  <http://ui.adsabs.harvard.edu/abs/2006ApJ...638L..51M>`_.

* Multiple ellipsoid bounds:
  `Feroz, Hobson & Bridges (2009)
  <http://ui.adsabs.harvard.edu/abs/2009MNRAS.398.1601F>`_.

* Overlapping balls/cubes:
  `Buchner (2016) <http://ui.adsabs.harvard.edu/abs/2014arXiv1407.5459B>`_ and
  `Buchner (2017) <https://ui.adsabs.harvard.edu/abs/2017arXiv170704476B>`_.

* Random walks/staggers:
  `Skilling (2006) <https://projecteuclid.org/euclid.ba/1340370944>`_.

* Multivariate/Random slice sampling:
  `Neal (2003) <https://projecteuclid.org/euclid.aos/1056562461>`_,
  `Handley, Hobson & Lasenby (2015a)
  <http://ui.adsabs.harvard.edu/abs/2015MNRAS.450L..61H>`_, and
  `Handley, Hobson & Lasenby (2015b)
  <http://ui.adsabs.harvard.edu/abs/2015MNRAS.453.4384H>`_.

* Hamiltonian/Reflective slice sampling:
  `Neal (2003) <https://projecteuclid.org/euclid.aos/1056562461>`_,
  `Skilling (2012) <https://aip.scitation.org/doi/abs/10.1063/1.3703630>`_,
  `Feroz & Skilling (2013)
  <https://ui.adsabs.harvard.edu/abs/2013AIPC.1553..106F>`_, and
  `Speagle (2019) <https://ui.adsabs.harvard.edu/abs/2019arXiv190402180S>`_.

If you have utilized some of the error analysis features available through
the provided utility functions (see :ref:`Nested Sampling Errors`),
you should also cite:

* Nested Sampling Errors:
  `Chopin & Robert (2010)
  <http://ui.adsabs.harvard.edu/abs/2008arXiv0801.3887C>`_,
  `Higson et al. (2018)
  <https://projecteuclid.org/euclid.ba/1508897094>`_, and
  `Speagle (2019)
  <https://ui.adsabs.harvard.edu/abs/2019arXiv190402180S>`_.

Code
====

``dynesty`` is the spiritual successor to Nested Sampling package `nestle 
<http://kylebarbary.com/nestle/>`_ and has benefited enormously from the work
put in by `Kyle Barbary <http://kylebarbary.com/>`_ and  `other contributors 
<https://github.com/joshspeagle/dynesty/blob/master/AUTHORS.md>`_.

Much of the API is inspired by the ensemble MCMC package
`emcee <https://emcee.readthedocs.io/en/stable/>`_ as well as other work by
`Daniel Foreman-Mackey <http://dfm.io/>`_.

Many of the plotting utilities draw heavily upon Daniel Foreman-Mackey's
wonderful `corner <http://corner.readthedocs.io>`_ package.

Several other plotting utilities as well as the real-time status outputs are
inspired in part by features available in the statistical modeling package
`PyMC3 <https://pymc-devs.github.io/pymc3/index.html>`_.

Papers and Texts
================

The dynamic sampling framework was entirely inspired by:

    `Higson et al. 2019 <https://doi.org/10.1007/s11222-018-9844-0>`_.
    *Dynamic nested sampling: an improved algorithm for parameter estimation
    and evidence calculation.*
    Stat Comput, 29, 891–913, doi:10.1007/s11222-018-9844-0.

Much of the nested sampling error analysis is based on:

    `Higson et al. 2018 <https://projecteuclid.org/euclid.ba/1508897094>`_.
    *Sampling errors in nested sampling parameter estimation.*
    Bayesian Analysis, 13, no. 3, 873--896, doi:10.1214/17-BA1075.

    `Chopin & Robert 2010
    <http://adsabs.harvard.edu/abs/2008arXiv0801.3887C>`_.
    *Properties of Nested Sampling.*
    Biometrika, 97, 741.

The nested sampling algorithms with cubes, balls bounds
are based on:

    `Buchner 2016 <http://adsabs.harvard.edu/abs/2014arXiv1407.5459B>`_.
    *A statistical test for Nested Sampling algorithms.*
    Statistics and Computing, 26, 383.

Slice sampling and its implementations in nested sampling are based on:

    `Handley, Hobson & Lasenby 2015b
    <http://adsabs.harvard.edu/abs/2015MNRAS.453.4384H>`_.
    *POLYCHORD: next-generation nested sampling.*
    MNRAS, 453, 4384.

    `Handley, Hobson & Lasenby 2015a
    <http://adsabs.harvard.edu/abs/2015MNRAS.450L..61H>`_.
    *POLYCHORD: nested sampling for cosmology.*
    MNRASL, 450, L61.

    `Neal 2003 <https://projecteuclid.org/euclid.aos/1056562461>`_.
    *Slice sampling.* Ann. Statist., 31, 705.

The implementation of multi-ellipsoidal decomposition are based in part on:

    `Feroz et al. 2013 <http://adsabs.harvard.edu/abs/2013arXiv1306.2144F>`_.
    *Importance Nested Sampling and the MultiNest Algorithm.*
    ArXiv e-prints, 1306.2144.

    `Feroz, Hobson & Bridges 2009
    <http://adsabs.harvard.edu/abs/2009MNRAS.398.1601F>`_.
    *MultiNest: an efficient and robust Bayesian inference tool for cosmology
    and particle physics.*
    MNRAS, 398, 1601.

Several useful reference texts include:

    `Salomone et al. 2018
    <https://arxiv.org/abs/1805.03924>`_.
    *Unbiased and Consistent Nested Sampling via Sequential Monte Carlo.*
    ArXiv e-prints, 1805.03924.

    `Walter 2015
    <https://arxiv.org/abs/1412.6368>`_.
    *Point Process-based Monte Carlo estimation.*
    ArXiv e-prints, 1412.6368.

    `Shaw, Bridges & Hobson 2007
    <http://adsabs.harvard.edu/abs/2007MNRAS.378.1365S>`_.
    *Efficient Bayesian inference for multimodal problems in cosmology.*
    MNRAS, 378, 1365.

    `Mukherjee, Parkinson & Liddle 2006
    <http://adsabs.harvard.edu/abs/2006ApJ...638L..51M>`_.
    *A Nested sampling algorithm for cosmological model selection.*
    ApJ, 638, L51.

    `Silvia & Skilling 2006
    <https://global.oup.com/academic/product/data-analysis-9780198568322>`_.
    *Data Analysis: A Bayesian Tutorial, 2nd Edition.*
    Oxford University Press.

    `Skilling 2006 <https://projecteuclid.org/euclid.ba/1340370944>`_.
    *Nested sampling for general Bayesian computation.*
    Bayesian Anal., 1, 833.

    `Skilling 2004 <http://adsabs.harvard.edu/abs/2004AIPC..735..395S>`_.
    *Nested Sampling.*
    In Maximum entropy and Bayesian methods in science and engineering
    (ed. G. Erickson, J.T. Rychert, C.R. Smith).
    AIP Conf. Proc., 735, 395.