6 papers
Fast approximate Bayesian multidimensional scaling with consistency guarantees
Ami Sheth, Aaron Smith, Andrew J. Holbrook
Bayesian multidimensional scaling (BMDS) embeds objects in a low-dimensional space to approximately preserve an observed dissimilarity matrix. Compared to classic MDS, BMDS is…
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…
Multiproposal Elliptical Slice Sampling
Guillermina Senn, Nathan Glatt-Holtz, Giulia Carigi +2
We introduce Multiproposal Elliptical Slice Sampling, a self-tuning multiproposal Markov chain Monte Carlo method for Bayesian inference with Gaussian priors. Our method generalize…
Bayesian Semi-Blind Deconvolution at Scale
Guillermina Senn, HÃ¥kon Tjelmeland, Nathan Glatt-Holtz +2
Blind image deconvolution refers to the problem of simultaneously estimating the blur kernel and the true image from a set of observations when both the blur kernel and the true im…
Sparse Bayesian multidimensional scaling(s)
Ami Sheth, Aaron Smith, Andrew J. Holbrook
Bayesian multidimensional scaling (BMDS) is a probabilistic dimension reduction tool that allows one to model and visualize data consisting of dissimilarities between pairs of obje…
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 ...