arXiv (All)
open access
The Arnoldi Aggregation for Approximate Transient Distributions of Markov Chains
The paper proposes a new aggregation method, based on the Arnoldi iteration, for computing approximate transient distributions of Markov chains. This aggregation is not partition-based, which means that an aggregate state may represent any portion of any original state, leading to a reduced system w
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
- Bayesian Analysis and Model Revision for kâth Order Markov Chains with Unknown k.#486203
- A signal dedispersion algorithm for imaging-based transient searches#652219
- Quantum-Enhanced Distributed Sensor Fusion: Lower Bounds on Aggregation from Projection Noise to Heisenberg-Limited Byzantine-Tolerant Networks#204758
- Order statistics in population protocols via simple dynamics#415093
- Kolmogorov--Arnold against bounded translations#818765
Record · ID 984059
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