arXiv (All)
open access
Parallel adaptive reweighting importance sampling for Bayesian astrophysics
Efficient sampling from high-dimensional, multi-modal posteriors is a central challenge in Bayesian inference across the physical sciences. Gravitational-wave (GW) astronomy, where likelihood evaluations are computationally expensive and posteriors are complex, is one application motivating this wor
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Massively parallel transdimensional sampling with data assimilation: an application to LISA galactic binaries#1011402
- FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology#1104511
- Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs#987245
- Benchmarking Exact, GP-Emulated, and Simulation-Based Inference for Late-Time Cosmology#1013593
- Nii-MALA: A Fast Metropolis-Adjusted Langevin Sampler with Radial Velocity Benchmarks#1084996
Record · ID 1076838
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive