169 citations · 390 across the 32 of their papers we have counts for
32 papers
Stochastic Gradient Annealed Importance Sampling for Efficient Online Marginal Likelihood Estimation
Scott A. Cameron, Hans C. Eggers, Steve Kroon
We consider estimating the marginal likelihood in settings with independent and identically distributed (i.i.d.) data. We propose estimating the predictive distributions in a seque…
Bayesian modelling of uncertainties of Monte Carlo radiative-transfer simulations
Frederik Beaujean, Hans C. Eggers, Wolfgang E. Kerzendorf
One of the big challenges in astrophysics is the comparison of complex simulations to observations. As many codes do not directly generate observables (e.g. hydrodynamic simulation…
Marginal Likelihoods from Monte Carlo Markov Chains
Alan Heavens, Yabebal Fantaye, Arrykrishna Mootoovaloo +4
In this paper, we present a method for computing the marginal likelihood, also known as the model likelihood or Bayesian evidence, from Markov Chain Monte Carlo (MCMC), or other sa…
No evidence for extensions to the standard cosmological model
Alan Heavens, Yabebal Fantaye, Elena Sellentin +4
We compute the Bayesian Evidence for models considered in the main analysis of Planck cosmic microwave background data. By utilising carefully-defined nearest-neighbour distances i…
Bayesian model comparison for one-dimensional azimuthal correlations in 200GeV AuAu collisions
Hans C. Eggers, Michiel B. de Kock, Thomas A. Trainor
In the context of data modeling and comparisons between different fit models, Bayesian analysis calls that model best which has the largest evidence, the prior-weighted integral ov…
Optimal modeling of 1D azimuth correlations in the context of Bayesian inference
Michiel B. De Kock, Hans C. Eggers, Thomas A. Trainor
Analysis and interpretation of spectrum and correlation data from high-energy nuclear collisions is currently controversial because two opposing physics narratives derive contradic…