Figshare
open access
CC BY 4.0
Computing the observed Fisher information matrix for linear Gaussian state space models and its application to specification tests
This article develops a recursive formula for computing the exact score vector and the observed Fisher information matrix in linear Gaussian state space models. The method provides closed-form analytical expressions for both first- and second-order derivatives, avoiding reliance on numerical diff
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.
2026-11-01T10:31:47Z https://api.figshare.com/v2/oai The request includes illegal arguments, is missing required arguments, includes a repeated argument, or values for arguments have an illegal syntax.
Related documents
- Non-Gaussian Parameter Bias: Formalism and Validation#1157452
- Gaussian processes for regression#245188
- Fast Generalized Neural Tangent Kernel Statistics via Trace Estimation#1144741
- Optimal information loss for Liouville spectral responses#1327911
- A Bayesian One-Sample Test for the Mean of Functional Data#1201525
Record · ID 1512951 · SHA-256 4f2dd1417a4a21e9
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