AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms

Kai Xu, Hong Ge, Will Tebbutt, Mohamed Tarek, Martin Trapp, Zoubin Ghahramani
Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, PMLR 118:1-10, 2020.

Abstract

Stan’s Hamilton Monte Carlo (HMC) has demonstrated remarkable sampling robustness and efficiency in a wide range of Bayesian inference problems through carefully crafted adaption schemes to the celebrated No-U-Turn sampler (NUTS) algorithm. It is challenging to implement these adaption schemes robustly in practice, hindering wider adoption amongst practitioners who are not directly working with the Stan modelling language. AdvancedHMC.jl (AHMC) contributes a modular, well-tested, standalone implementation of NUTS that recovers and extends Stan’s NUTS algorithm. AHMC is written in Julia, a modern high-level language for scientic computing, benefoting from optional hardware acceleration and interoperability with a wealth of existing software written in both Julia and other languages, such as Python. Efficacy is demonstrated empirically by comparison with Stan through a third-party Markov chain Monte Carlo benchmarking suite.

Cite this Paper

BibTeX
@InProceedings{pmlr-v118-xu20a, title = {AdvancedHMC.jl: A robust, modular and ecient implementation of advanced HMC algorithms }, author = {Xu, Kai and Ge, Hong and Tebbutt, Will and Tarek, Mohamed and Trapp, Martin and Ghahramani, Zoubin}, booktitle = {Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference}, pages = {1--10}, year = {2020}, editor = {Zhang, Cheng and Ruiz, Francisco and Bui, Thang and Dieng, Adji Bousso and Liang, Dawen}, volume = {118}, series = {Proceedings of Machine Learning Research}, month = {08 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v118/xu20a/xu20a.pdf}, url = {https://proceedings.mlr.press/v118/xu20a.html}, abstract = {Stan’s Hamilton Monte Carlo (HMC) has demonstrated remarkable sampling robustness and efficiency in a wide range of Bayesian inference problems through carefully crafted adaption schemes to the celebrated No-U-Turn sampler (NUTS) algorithm. It is challenging to implement these adaption schemes robustly in practice, hindering wider adoption amongst practitioners who are not directly working with the Stan modelling language. AdvancedHMC.jl (AHMC) contributes a modular, well-tested, standalone implementation of NUTS that recovers and extends Stan’s NUTS algorithm. AHMC is written in Julia, a modern high-level language for scientic computing, benefoting from optional hardware acceleration and interoperability with a wealth of existing software written in both Julia and other languages, such as Python. Efficacy is demonstrated empirically by comparison with Stan through a third-party Markov chain Monte Carlo benchmarking suite.} }
Endnote
%0 Conference Paper %T AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms %A Kai Xu %A Hong Ge %A Will Tebbutt %A Mohamed Tarek %A Martin Trapp %A Zoubin Ghahramani %B Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference %C Proceedings of Machine Learning Research %D 2020 %E Cheng Zhang %E Francisco Ruiz %E Thang Bui %E Adji Bousso Dieng %E Dawen Liang %F pmlr-v118-xu20a %I PMLR %P 1--10 %U https://proceedings.mlr.press/v118/xu20a.html %V 118 %X Stan’s Hamilton Monte Carlo (HMC) has demonstrated remarkable sampling robustness and efficiency in a wide range of Bayesian inference problems through carefully crafted adaption schemes to the celebrated No-U-Turn sampler (NUTS) algorithm. It is challenging to implement these adaption schemes robustly in practice, hindering wider adoption amongst practitioners who are not directly working with the Stan modelling language. AdvancedHMC.jl (AHMC) contributes a modular, well-tested, standalone implementation of NUTS that recovers and extends Stan’s NUTS algorithm. AHMC is written in Julia, a modern high-level language for scientic computing, benefoting from optional hardware acceleration and interoperability with a wealth of existing software written in both Julia and other languages, such as Python. Efficacy is demonstrated empirically by comparison with Stan through a third-party Markov chain Monte Carlo benchmarking suite.
APA
Xu, K., Ge, H., Tebbutt, W., Tarek, M., Trapp, M. & Ghahramani, Z.. (2020). AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms . Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, in Proceedings of Machine Learning Research 118:1-10 Available from https://proceedings.mlr.press/v118/xu20a.html.

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