gemlib.mcmc supplies composable kernel abstractions for Metropolis-within-Gibbs sampling via writer monads, allowing concise expression and reuse of complex MCMC algorithms for partially observed epidemic models.
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SFB basis applied to eBOSS DR16 samples identifies evidence (p<0.005 vs EZMocks) of stellar contamination systematics at large scales in QSOs and unknown systematics at plate/imaging scales in both LRG and QSO samples via fNL inconsistencies.
Bayesian nonparametric mixtures of Poisson and normal regressions using DP and PY priors are fitted via MCMC to predict claims frequency and severity, with an illustration on French motor insurance data.
citing papers explorer
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gemlib.mcmc: composable kernels for Metropolis-within-Gibbs sampling schemes
gemlib.mcmc supplies composable kernel abstractions for Metropolis-within-Gibbs sampling via writer monads, allowing concise expression and reuse of complex MCMC algorithms for partially observed epidemic models.
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Demonstrating the Use of the Spherical Fourier Bessel Basis for Large Scale Clustering Systematics Discovery and Mitigation with eBOSS
SFB basis applied to eBOSS DR16 samples identifies evidence (p<0.005 vs EZMocks) of stellar contamination systematics at large scales in QSOs and unknown systematics at plate/imaging scales in both LRG and QSO samples via fNL inconsistencies.
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Modeling Insurance Claims using Bayesian Nonparametric Regression
Bayesian nonparametric mixtures of Poisson and normal regressions using DP and PY priors are fitted via MCMC to predict claims frequency and severity, with an illustration on French motor insurance data.