From bfc81db23faf0ccf397ac2db6615eacb981f3e8f Mon Sep 17 00:00:00 2001 From: Hong Ge <3279477+yebai@users.noreply.github.com> Date: Fri, 20 Oct 2023 20:06:17 +0100 Subject: [PATCH] Update external-samplers.jmd --- .../external-samplers.jmd | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/tutorials/docs-16-using-turing-external-samplers/external-samplers.jmd b/tutorials/docs-16-using-turing-external-samplers/external-samplers.jmd index 858798adf..e6b9a2b13 100644 --- a/tutorials/docs-16-using-turing-external-samplers/external-samplers.jmd +++ b/tutorials/docs-16-using-turing-external-samplers/external-samplers.jmd @@ -151,19 +151,19 @@ There are several characteristics to note in these functions: - The functions must follow the displayed signatures. - The output of the functions must be a transition, the current state of the sampler, and a sample, what is saved to the MCMC chain. -The last requirement is that the transition must be structured with a field `θ` which contains the values of the parameters of the model for said transition. +The last requirement is that the transition must be structured with a field `θ`, which contains the values of the parameters of the model for said transition. This allows `Turing` to seamlessly extract the parameter values at each step of the chain when bundling the chains. -Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail. +Note that if the external sampler produces transitions that Turing cannot parse, the bundling of the samples will be different or fail. For practical examples of how to adapt a sampling library to the `AbstractMCMC` interface, the readers can consult the following libraries: - - AdvancedMH - - AdvancedHMC - - MicroCanonicalHMC + - [AdvancedMH](https://github.com/TuringLang/AdvancedMH.jl/blob/458a602ac32a8514a117d4c671396a9ba8acbdab/src/mh-core.jl#L73-L115) + - [AdvancedHMC](https://github.com/TuringLang/AdvancedHMC.jl/blob/762e55f894d142495a41a6eba0eed9201da0a600/src/abstractmcmc.jl#L102-L170) + - [MicroCanonicalHMC](https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl) # Refences -[^1]: Xu et al, AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms, 2019 -[^2]: Zhang et al, Pathfinder: Parallel quasi-Newton variational inference , 2021 -[^3]: Robnik et al, Microcanonical Hamiltonian Monte Carlo, 2022 -[^4]: Robnik and Seljak, Langevine Hamiltonian Monte Carlo, 2023 +[^1]: Xu et al., [AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms](http://proceedings.mlr.press/v118/xu20a/xu20a.pdf), 2019 +[^2]: Zhang et al., [Pathfinder: Parallel quasi-Newton variational inference](https://arxiv.org/abs/2108.03782), 2021 +[^3]: Robnik et al, [Microcanonical Hamiltonian Monte Carlo](https://arxiv.org/abs/2212.08549), 2022 +[^4]: Robnik and Seljak, [Langevine Hamiltonian Monte Carlo](https://arxiv.org/abs/2303.18221), 2023