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    Por favor, use este identificador para citar o enlazar este ítem:http://uvadoc.uva.es/handle/10324/28850

    Título
    Randomized Hamiltonian Monte Carlo
    Autor
    Bou-Rabee, Nawaf
    Sanz Serna, Jesús MaríaAutoridad UVA Orcid
    Año del Documento
    2017
    Documento Fuente
    The Annals of Applied Probability, 2017, Volume 27, Number 4, p. 2159-2194.
    Zusammenfassung
    Tuning the durations of the Hamiltonian flow in Hamiltonian Monte Carlo (also called Hybrid Monte Carlo) (HMC) involves a tradeoff between computational cost and sampling quality, which is typically challenging to resolve in a satisfactory way. In this article, we present and analyze a randomized HMC method (RHMC), in which these durations are i.i.d. exponential random variables whose mean is a free parameter. We focus on the small time step size limit, where the algorithm is rejection-free and the computational cost is proportional to the mean duration. In this limit, we prove that RHMC is geometrically ergodic under the same conditions that imply geometric ergodicity of the solution to underdamped Langevin equations. Moreover, in the context of a multidimensional Gaussian distribution, we prove that the sampling efficiency of RHMC, unlike that of constant duration HMC, behaves in a regular way. This regularity is also verified numerically in non-Gaussian target distributions. Finally, we suggest variants of RHMC for which the time step size is not required to be small.
    ISSN
    1050-5164
    Revisión por pares
    SI
    DOI
    10.1214/16-AAP1255
    Patrocinador
    Ministerio de Economía, Industria y Competitividad (Project MTM2013-46553-C3-1-P).
    Version del Editor
    https://projecteuclid.org/euclid.aoap/1504080029
    Idioma
    eng
    URI
    http://uvadoc.uva.es/handle/10324/28850
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP51 - Artículos de revista [145]
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