@article{betancourt_conceptual_2017,
	title = {A {Conceptual} {Introduction} to {Hamiltonian} {Monte} {Carlo}},
	volume = {1701},
	url = {http://adsabs.harvard.edu/abs/2017arXiv170102434B},
	abstract = {Hamiltonian Monte Carlo has proven a remarkable empirical success, but only recently have we begun to develop a rigorous under- standing of why it performs so well on difficult problems and how it is best applied in practice. Unfortunately, that understanding is con- fined within the mathematics of differential geometry which has limited its
dissemination, especially to the applied communities for which it is particularly important. In this review I provide a comprehensive conceptual account of these theoretical foundations, focusing on developing a principled intuition behind the method and its optimal implementations rather of any ex- haustive rigor. Whether a practitioner or a statistician, the dedicated reader will acquire a solid grasp of how Hamiltonian Monte Carlo works, when it succeeds, and, perhaps most importantly, when it fails.},
	urldate = {2017-01-29},
	journal = {ArXiv e-prints},
	author = {Betancourt, Michael},
	month = jan,
	year = {2017},
	keywords = {Statistics - Methodology},
	pages = {arXiv:1701.02434},
}

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