9 citations · 11 across the 4 of their papers we have counts for
13 papers
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…
Parallel Tempering on Optimized Paths
Saifuddin Syed, Vittorio Romaniello, Trevor Campbell +1
Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithms that constructs a path of distributions annealing between a tractable reference and an intractable target,…
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
Sina Amini Niaki, Ehsan Haghighat, Trevor Campbell +2
We present a Physics-Informed Neural Network (PINN) to simulate the thermochemical evolution of a composite material on a tool undergoing cure in an autoclave. In particular, we so…
Slice Sampling for General Completely Random Measures
Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell
Completely random measures provide a principled approach to creating flexible unsupervised models, where the number of latent features is infinite and the number of features that i…
Validated Variational Inference via Practical Posterior Error Bounds
Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell +1
Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to th…
Sparse Variational Inference: Bayesian Coresets from Scratch
Trevor Campbell, Boyan Beronov
The proliferation of automated inference algorithms in Bayesian statistics has provided practitioners newfound access to fast, reproducible data analysis and powerful statistical m…