265 citations · 725 across the 26 of their papers we have counts for
8 papers · 1 filter
Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success
Richard Scalzo, David Kohn, Hugo Olierook +4
The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multi-modal; propert…
Surrogate-assisted Bayesian inversion for landscape and basin evolution models
Rohitash Chandra, Danial Azam, Arpit Kapoor +1
The complex and computationally expensive nature of landscape evolution models pose significant challenges in the inference and optimisation of unknown parameters. Bayesian inferen…
Langevin-gradient parallel tempering for Bayesian neural learning
Rohitash Chandra, Konark Jain, Ratneel V. Deo +1
Bayesian neural learning feature a rigorous approach to estimation and uncertainty quantification via the posterior distribution of weights that represent knowledge of the neural n…
Surrogate-assisted parallel tempering for Bayesian neural learning
Rohitash Chandra, Konark Jain, Arpit Kapoor +1
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) metho…
Computer vision-based framework for extracting geological lineaments from optical remote sensing data
Ehsan Farahbakhsh, Rohitash Chandra, Hugo K. H. Olierook +4
The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes a…
Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics
Jodie Pall, Rohitash Chandra, Danial Azam +4
Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic…