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https://github.com/red-portal/reversiblejump.jl

Reversible Jump MCMC Algorithms in Julia
https://github.com/red-portal/reversiblejump.jl

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Reversible Jump MCMC Algorithms in Julia

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# ReversibleJump

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A package providing reversible jump MCMC algorithms.
In particular, it provides the following implementations:

* The original algorithm by Green with independent jump proposals[^G1995],
* the annealed importance sampling jump proposals by Karagiannis and Andrieu[^KA2013], and
* the non-reversible jump proposals by Gagnon and Doucet[^GD2020].

The exactness of the implementations are tested using the package [MCMCTesting.jl](https://github.com/Red-Portal/MCMCTesting.jl), which implements the hypothesis-testing approach by Gandy and Scott[^GS2020].

For the jump moves, the package currently only provides (unsorted) birth and death.
But additional moves such as sorted birth-death and split-merge, are planned to be added in the future.

## Example
An example of using the package is provided in `SinusoidDetection/`.
It contains a replication of the sinusoid joint estimation and detection model by Andrieu and Doucet [^AD1999].

[^G1995]: Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711-732.
[^KA2013]: Karagiannis, G., & Andrieu, C. (2013). Annealed importance sampling reversible jump MCMC algorithms. Journal of Computational and Graphical Statistics, 22(3), 623-648.
[^GD2020]: Gagnon, P., & Doucet, A. (2020). Nonreversible jump algorithms for Bayesian nested model selection. Journal of Computational and Graphical Statistics, 30(2), 312-323.
[^AD1999]: Andrieu, C., & Doucet, A. (1999). Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC. IEEE Transactions on Signal Processing, 47(10), 2667-2676.
[^GS2020]: Gandy, Axel, and James Scott. "Unit testing for MCMC and other Monte Carlo methods." arXiv preprint arXiv:2001.06465 (2020).