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Found 3 papers, 3 papers with code
The future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
Standard nested sampling techniques are simply not feasible in this high-dimensional setting, requiring a projected 12 years of compute time on 48 CPU cores; on the other hand, the proposed approach only requires 8 days of compute time on 24 GPUs.
Learned harmonic mean estimation of the marginal likelihood with normalizing flows
The learned harmonic mean estimator solves the exploding variance problem of the original harmonic mean estimation of the marginal likelihood.
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism
We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows complete and fast event locations for $\textit{any}$ source mechanism.
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