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, Håkon 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…
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 ...
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…