Laplacian Hamiltonian Monte Carlo

Yizhe Zhang; Changyou Chen; Ricardo Henao; Lawrence Carin

doi:10.1007/978-3-319-46128-1_7

Laplacian Hamiltonian Monte Carlo

Yizhe Zhang, Changyou Chen, Ricardo Henao, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We proposed a Hamiltonian Monte Carlo (HMC) method with Laplace kinetic energy, and demonstrate the connection between slice sampling and proposed HMC method in one-dimensional cases. Based on this connection, one can perform slice sampling using a numerical integrator in an HMC fashion. We provide theoretical analysis on the performance of such sampler in several univariate cases. Furthermore, the proposed approach extends the standard HMC by enabling sampling from discrete distributions. We compared our method with standard HMC on both synthetic and real data, and discuss its limitations and potential improvements.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer [email protected]
Pages	98-114
Number of pages	17
ISBN (Print)	9783319461274
DOIs	https://doi.org/10.1007/978-3-319-46128-1_7
State	Published - Jan 1 2016
Externally published	Yes

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10.1007/978-3-319-46128-1_7

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title = "Laplacian Hamiltonian Monte Carlo",

abstract = "We proposed a Hamiltonian Monte Carlo (HMC) method with Laplace kinetic energy, and demonstrate the connection between slice sampling and proposed HMC method in one-dimensional cases. Based on this connection, one can perform slice sampling using a numerical integrator in an HMC fashion. We provide theoretical analysis on the performance of such sampler in several univariate cases. Furthermore, the proposed approach extends the standard HMC by enabling sampling from discrete distributions. We compared our method with standard HMC on both synthetic and real data, and discuss its limitations and potential improvements.",

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T1 - Laplacian Hamiltonian Monte Carlo

AU - Zhang, Yizhe

AU - Chen, Changyou

AU - Henao, Ricardo

AU - Carin, Lawrence

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PY - 2016/1/1

Y1 - 2016/1/1

N2 - We proposed a Hamiltonian Monte Carlo (HMC) method with Laplace kinetic energy, and demonstrate the connection between slice sampling and proposed HMC method in one-dimensional cases. Based on this connection, one can perform slice sampling using a numerical integrator in an HMC fashion. We provide theoretical analysis on the performance of such sampler in several univariate cases. Furthermore, the proposed approach extends the standard HMC by enabling sampling from discrete distributions. We compared our method with standard HMC on both synthetic and real data, and discuss its limitations and potential improvements.

AB - We proposed a Hamiltonian Monte Carlo (HMC) method with Laplace kinetic energy, and demonstrate the connection between slice sampling and proposed HMC method in one-dimensional cases. Based on this connection, one can perform slice sampling using a numerical integrator in an HMC fashion. We provide theoretical analysis on the performance of such sampler in several univariate cases. Furthermore, the proposed approach extends the standard HMC by enabling sampling from discrete distributions. We compared our method with standard HMC on both synthetic and real data, and discuss its limitations and potential improvements.

UR - http://link.springer.com/10.1007/978-3-319-46128-1_7

UR - http://www.scopus.com/inward/record.url?scp=84988664016&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46128-1_7

DO - 10.1007/978-3-319-46128-1_7

M3 - Conference contribution

SN - 9783319461274

SP - 98

EP - 114

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer [email protected]

ER -

Laplacian Hamiltonian Monte Carlo

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