Citation

BibTex format

@article{Barp:2018:10.1146/annurev-statistics-031017-100141,
author = {Barp, A and Briol, F-X and Kennedy, AD and Girolami, M},
doi = {10.1146/annurev-statistics-031017-100141},
journal = {Annual Review of Statistics and Its Application},
pages = {451--471},
title = {Geometry and dynamics for Markov chain Monte Carlo},
url = {http://dx.doi.org/10.1146/annurev-statistics-031017-100141},
volume = {5},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Markov Chain Monte Carlo methods have revolutionised mathematical computationand enabled statistical inference within many previously intractable models. Inthis context, Hamiltonian dynamics have been proposed as an efficient way ofbuilding chains which can explore probability densities efficiently. The methodemerges from physics and geometry and these links have been extensively studiedby a series of authors through the last thirty years. However, there iscurrently a gap between the intuitions and knowledge of users of themethodology and our deep understanding of these theoretical foundations. Theaim of this review is to provide a comprehensive introduction to the geometrictools used in Hamiltonian Monte Carlo at a level accessible to statisticians,machine learners and other users of the methodology with only a basicunderstanding of Monte Carlo methods. This will be complemented with somediscussion of the most recent advances in the field which we believe willbecome increasingly relevant to applied scientists.
AU - Barp,A
AU - Briol,F-X
AU - Kennedy,AD
AU - Girolami,M
DO - 10.1146/annurev-statistics-031017-100141
EP - 471
PY - 2018///
SN - 2326-8298
SP - 451
TI - Geometry and dynamics for Markov chain Monte Carlo
T2 - Annual Review of Statistics and Its Application
UR - http://dx.doi.org/10.1146/annurev-statistics-031017-100141
UR - http://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-031017-100141
UR - http://hdl.handle.net/10044/1/53201
VL - 5
ER -