169 citations · 353 across the 12 of their papers we have counts for
5 papers · 1 filter
Assessment of Gradient-Based Samplers in Standard Cosmological Likelihoods
Arrykrishna Mootoovaloo, Jaime Ruiz-Zapatero, Carlos García-García +1
We assess the usefulness of gradient-based samplers, such as the No-U-Turn Sampler (NUTS), by comparison with traditional Metropolis-Hastings algorithms, in tomographic $3 \times 2…
Extreme data compression for Bayesian model comparison
Alan F. Heavens, Arrykrishna Mootoovaloo, Roberto Trotta +1
We develop extreme data compression for use in Bayesian model comparison via the MOPED algorithm, as well as more general score compression. We find that Bayes factors from data co…
Imbalance Learning for Variable Star Classification
Zafiirah Hosenie, Robert Lyon, Benjamin Stappers +2
The accurate automated classification of variable stars into their respective sub-types is difficult. Machine learning based solutions often fall foul of the imbalanced learning pr…
Comparing Multi-class, Binary and Hierarchical Machine Learning Classification schemes for variable stars
Zafiirah Hosenie, Robert Lyon, Benjamin Stappers +1
Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels f…
Bayes Factors via Savage-Dickey Supermodels
A. Mootoovaloo, Bruce A. Bassett, M. Kunz
We outline a new method to compute the Bayes Factor for model selection which bypasses the Bayesian Evidence. Our method combines multiple models into a single, nested, Supermodel…