A Markov Jump Process for More Efficient Hamiltonian Monte Carlo
By A. Berger, Mayur Mudigonda, Michael Robert DeWeese, Jascha Narain Sohl-Dickstein. 2015
In most sampling algorithms, including Hamiltonian Monte Carlo, transition rates between states correspond to the probability of making a transition in a single time step, and are constrained to be less than or equal to 1. We derive a Hamiltonian Monte Carlo algorithm using a continuous time Markov jump process, and are thus able to escape this constraint. Transition rates in a Markov jump process need only be non-negative. We demonstrate that the new algorithm leads to improved mixing for several example problems, both by evaluating the spectral gap of the Markov operator, and by computing autocorrelation as a function of compute time. We release the algorithm as an open source Python package.
Subfields: Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Probabilistic & Bayesian Methods
- Matthew D. Hoffman
- Jascha Narain Sohl-Dickstein
- Daniel Lévy
- Stefano Ermon
- David M. Blei
- Yang Song
- Diederik P. Kingma
- Jun Zhu
Citations in the corpus
- Generalizing Hamiltonian Monte Carlo with Neural Networks (2017, methodology): “, 2009; Betancourt, 2013); with the use of alternative HMC accept-reject rules (Sohl-Dickstein et al., 2014; Berger et al., 2015); with the use of non-canonical Hamiltonian dynamics (Tripuraneni et al.”