Pocomc A Python Package For Accelerated Bayesian

pocoMC: A Python package for accelerated Bayesian inference in ....

pocoMC is a Python package for accelerated Bayesian inference in astronomy and cosmology. The code is designed to sample efficiently from posterior distributions with non-trivial geometry, including strong multimodality and non-linearity. To this end, pocoMC relies on the Preconditioned Monte Carlo ....

https://www.bibsonomy.org/bibtex/34b91e60393ec62c6ddc35a2e0c55951.

pocoMC: A Python package for accelerated Bayesian inference in ....

Jul 13, 2022 . pocoMC is a Python package for accelerated Bayesian inference in astronomy and cosmology. The code is designed to sample efficiently from posterior distributions with non-trivial geometry, including strong multimodality and non-linearity..

https://www.bibsonomy.org/bibtex/21561d04f1cc49d49cc1acc82a7d9334f/gpkulkarni.

[2207.05660] pocoMC: A Python package for accelerated Bayesian ....

Jul 12, 2022 . pocoMC is a Python package for accelerated Bayesian inference in astronomy and cosmology. The code is designed to sample efficiently from posterior distributions with non-trivial geometry, including strong multimodality and non-linearity. To this end, pocoMC relies on the Preconditioned Monte Carlo algorithm which utilises a Normalising Flow in order to ....

https://arxiv.org/abs/2207.05660.

GitHub - minaskar/pocomc: pocoMC: A Python implementation ….

pocoMC is a Python implementation of the Preconditioned Monte Carlo method for accelerated Bayesian inference. Getting started Brief introduction. pocoMC utilises a Normalising Flow in order to precondition the target distribution by removing any.

https://github.com/minaskar/pocomc.

pocoMC: A Python package for accelerated Bayesian.

pocoMC is a Python package for accelerated Bayesian inference in astron-omy and cosmology. The code is designed to sample e ciently from posterior distributions with non{trivial geometry, including strong multimodality and non{linearity. To this end, pocoMC relies on the Preconditioned Monte Carlo.

https://arxiv.org/pdf/2207.05660.