HAL (France)
open access
Evolutionary Sequential Monte Carlo Samplers for Change-Point Models
Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only do SMC algor
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
- SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributions#1116150
- Correlation-Free Transition Path Sampling through Shooting Point Generation Guided by Committor Learning#1034278
- Correlation-Free Transition Path Sampling through Shooting Point Generation Guided by Committor Learning#978442
- Spectra: Exact Component Transport for Test-Time Prior Adaptation in Simulation-Based Inference#1217756
- Adaptive proposals for nonparametric equation-of-state inference with RIFT: iterative Gaussian-process refinement in a compressed sequence representation#1216799
Record · ID 1235717
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