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[ascl:2305.011]
DarkMappy: Mapping the dark universe

DarkMappy reconstructs maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem in order to implement hybrid Bayesian dark-matter reconstruction techniques on the plane and on the celestial sphere. These convergence maps support principled uncertainty quantification and provide hypothesis testing of structure, from which it is possible to distinguish between physical objects and artifacts of the reconstruction.

[ascl:2401.009]
Harmonic: Learnt harmonic mean estimator

McEwen, Jason D.; Wallis, Christopher G. R.; Price, Matthew A.; Docherty, Matthew M.; Spurio Mancini, Alessio

harmonic learns an approximate harmonic mean estimator (referred to as a "learnt harmonic mean estimator") from posterior distribution samples to compute the marginal likelihood required for Bayesian model selection. Using a large number of independent Markov chain Monte Carlo (MCMC) chains from another package such as emcee (ascl:1303.002), harmonic uses importance sampling to learn a new target distribution in order to optimize an approximate harmonic estimator while minimizing its variance.

[ascl:2401.017]
QuantifAI: Radio interferometric imaging reconstruction with scalable Bayesian uncertainty quantification

Liaudat, TobĂas I.; Mars, Matthijs; Price, Matthew A.; Pereyra, Marcelo; Betcke, Marta M.; McEwen, Jason D.

QuantifAI reconstructs radio interferometric images using scalable Bayesian uncertainty quantification relying on data-driven (learned) priors. It relies on the convex accelerated optimization algorithms in CRR (ascl:2401.016) and is built on top of PyTorch. QuantifAI also includes MCMC algorithms for posterior sampling.