12 citations · 35 across the 11 of their papers we have counts for
12 papers
Mad Props: Parallelism in Markov Chain Monte Carlo Through the Lens of the Infinite Proposal Limit
Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis +1
Multiproposal MCMC (MP-MCMC) algorithms use clouds of proposals to efficiently traverse state spaces and overcome complex target geometries. While MCMC methods are embarrassingly p…
Sacred and Profane: from the Involutive Theory of MCMC to Helpful Hamiltonian Hacks
Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis +2
In the first edition of this Handbook, two remarkable chapters consider seemingly distinct yet deeply connected subjects ...
A tale of two faults: Statistical reconstruction of the 1820 Flores Sea earthquake using tsunami observations alone
T. Paskett, J. P. Whitehead, R. A. Harris +4
Using a Bayesian approach we compare anecdotal tsunami runup observations from the 29 December 1820 Flores Sea earthquake with close to 200,000 tsunami simulations to determine the…
A Statistical Framework for Domain Shape Estimation in Stokes Flows
Jeff Borggaard, Nathan E. Glatt-Holtz, Justin A. Krometis
We develop and implement a Bayesian approach for the estimation of the shape of a two dimensional annular domain enclosing a Stokes flow from sparse and noisy observations of the e…
Parallel MCMC Algorithms: Theoretical Foundations, Algorithm Design, Case Studies
Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis +1
Parallel Markov Chain Monte Carlo (pMCMC) algorithms generate clouds of proposals at each step to efficiently resolve a target probability distribution. We build a rigorous foundat…
Embracing Uncertainty in "Small Data" Problems: Estimating Earthquakes from Historical Anecdotes
Nathan E. Glatt-Holtz, Ronald A. Harris, Andrew J. Holbrook +4
Seismic risk estimates will be vastly improved with an increased understanding of historical (and pre-historical) seismic events. However the only existing data for these events is…