408 citations · 790 across the 12 of their papers we have counts for
5 papers · 1 filter
Hamiltonian Monte Carlo in Inverse Problems; Ill-Conditioning and Multi-Modality
Ian Langmore, Michael Dikovsky, Scott Geraedts +2
The Hamiltonian Monte Carlo (HMC) method allows sampling from continuous densities. Favorable scaling with dimension has led to wide adoption of HMC by the statistics community. Mo…
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware
Junpeng Lao, Christopher Suter, Ian Langmore +7
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century. Its guarantees of asymptotic convergence, stability, and estimator-v…
A Condition Number for Hamiltonian Monte Carlo
Ian Langmore, Michael Dikovsky, Scott Geraedts +2
Hamiltonian Monte Carlo is a popular sampling technique for smooth target densities. The scale lengths of the target have long been known to influence integration error and samplin…
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon +3
Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions. However, when the geometry of the posterior is unfavorable, it may…
Quadrature Compound: An approximating family of distributions
Josh Dillon, Ian Langmore
Compound distributions allow construction of a rich set of distributions. Typically they involve an intractable integral. Here we use a quadrature approximation to that integral to…