6 citations · 6 across the 4 of their papers we have counts for
6 papers
GPU-accelerated Bayesian inference for block-cave geometry recovery via muon tomography
Miguel Biron-Lattes, Patrick Belliveau, Faezeh Yazdi +4
We describe a Bayesian framework for the inverse problem of geometry recovery of block caving via muon tomography. We work with a low dimensional surface-based representation of th…
On the Orbit of the Binary Brown Dwarf Companion GL229 Ba and Bb
William Thompson, Dori Blakely, Jerry W. Xuan +21
The companion GL229B was recently resolved by Xuan et al. (2024) as a tight binary of two brown dwarfs (Ba and Bb) through VLTI-GRAVITY interferometry and VLT-CRIRES+ RV measuremen…
AutoStep: Locally adaptive involutive MCMC
Tiange Liu, Nikola Surjanovic, Miguel Biron-Lattes +2
Many common Markov chain Monte Carlo (MCMC) kernels can be formulated using a deterministic involutive proposal with a step size parameter. Selecting an appropriate step size is of…
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?
Son Luu, Zuheng Xu, Nikola Surjanovic +3
The Hamiltonian Monte Carlo (HMC) algorithm is often lauded for its ability to effectively sample from high-dimensional distributions. In this paper we challenge the presumed domin…
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed +2
Selecting the step size for the Metropolis-adjusted Langevin algorithm (MALA) is necessary in order to obtain satisfactory performance. However, finding an adequate step size for a…
Pseudo-marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations
Miguel Biron-Lattes, Alexandre Bouchard-Côté, Trevor Campbell
Bayesian inference for Continuous-Time Markov Chains (CTMCs) on countably infinite spaces is notoriously difficult because evaluating the likelihood exactly is intractable. One way…