REVIEW 8 cited by
pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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 decorrelate the parameters of the posterior. It facilitates both tasks of parameter estimation and model comparison, focusing especially on computationally expensive applications. It allows fitting arbitrary models defined as a log-likelihood function and a log-prior probability density function in Python. Compared to popular alternatives (e.g. nested sampling) pocoMC can speed up the sampling procedure by orders of magnitude, cutting down the computational cost substantially. Finally, parallelisation to computing clusters manifests linear scaling.
Forward citations
Cited by 8 Pith papers
-
Correlated signals of ultralight scalar dark matter in pulsar timing
A finite-spatial-correlation Gaussian-field prior for PTA ULDM signals interpolates between fully correlated and uncorrelated limits and is validated on blinded mock data for linear and quadratic couplings.
-
Ab Initio Real-Time Gravitational-Wave Parameter Estimation
Slice-within-Gibbs nested sampling on modern GPUs delivers well-calibrated BNS parameter estimation in ~12 minutes uncompressed and ~89 seconds with heterodyning, from cold priors.
-
First planetesimals from DESI DR1: 12 highly metal-rich white dwarfs
Twelve white dwarfs from DESI DR1 have accreted debris resembling inner-Solar-System rock, with two systems likely accreting water-rich planetesimals.
-
Cosmic Shear constraints from HSC Year 3 with clustering calibration of the tomographic redshift distributions from DESI
Reanalysis of HSC Y3 cosmic shear with DESI clustering redshift calibration yields S8 = 0.805 ± 0.018, a 1.8× error reduction and upward shift toward Planck cosmology.
-
Accelerated Sequential Posterior Inference via Reuse for Gravitational-Wave Analyses
ASPIRE reuses old posterior samples via normalizing flows and sequential Monte Carlo to produce unbiased posteriors and evidences under new models, cutting likelihood evaluations 4-10x.
-
Progress toward the detection of the gravitational-wave background from stellar-mass binary black holes: a mock data challenge
A mock data challenge shows that a phase-coherent search for the binary black hole background can recover injected signal fractions in realistic noise, using new treatments of noise uncertainty, finite-duration effect...
-
MNE: overparametrized neural evolution with applications to diffusion processes and sampling
Minimal neural evolution is a sketching-based way to evolve neural network parameters so the network solves a target equation at sample points, and it samples from densities via Ornstein-Uhlenbeck diffusion with bias ...
-
Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis
A heavy-tailed hyperbolic likelihood, applied across the full frequency band, gives gravitational-wave parameter estimates that are as good as standard methods in Gaussian noise and less biased in glitchy or overlappi...
Discussion (0). Continue with ORCID to comment.