On the application of improved symplectic integrators in Hamiltonian Monte Carlo. Issue 2 (7th February 2018)

• Record Type: Journal Article
• Authors: Mannseth, Janne; Kleppe, Tore S.; Skaug, Hans J.
• Is Part Of: Communications in statistics. Volume 47:Issue 2(2018)
• Page Start: 500
• Page End: 509
• Publication Date: 2018-02-07
• Subjects: Efficient Sample Size -- Hamiltonian Monte Carlo -- Integration scheme -- Leapfrog method -- No-U-Turn Sampler -- Parameterized numerical scheme; Primary 62F15 Bayesian inference -- Secondary 65P10 Hamiltonian systems including symplectic integrators; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2017.1283703
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: We explore the construction of new symplectic numerical integration schemes to be used in Hamiltonian Monte Carlo and study their efficiency. Integration schemes from Blanes et al., and a new scheme are considered as candidates to the commonly used leapfrog method. All integration schemes are tested within the framework of the No-U-Turn sampler (NUTS), both for a logistic regression model and a student t -model. The results show that the leapfrog method is inferior to all the new methods both in terms of asymptotic expected acceptance probability for a model problem and the efficient sample size per computing time for the realistic models.