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Optimizing The Integrator Step Size for Hamiltonian Monte Carlo
M. J. Betancourt, Simon Byrne, Mark Girolami
Hamiltonian Monte Carlo can provide powerful inference in complex statistical problems, but ultimately its performance is sensitive to various tuning parameters. In this paper we u…
The Geometric Foundations of Hamiltonian Monte Carlo
M. J. Betancourt, Simon Byrne, Samuel Livingstone +1
Although Hamiltonian Monte Carlo has proven an empirical success, the lack of a rigorous theoretical understanding of the algorithm has in many ways impeded both principled develop…
Adiabatic Monte Carlo
M. J. Betancourt
A common strategy for inference in complex models is the relaxation of a simple model into the more complex target model, for example the prior into the posterior in Bayesian infer…
Unsupervised Transient Light Curve Analysis Via Hierarchical Bayesian Inference
Nathan Sanders, Michael Betancourt, Alicia Soderberg
Historically, light curve studies of supernovae (SNe) and other transient classes have focused on individual objects with copious and high signal-to-noise observations. In the nasc…