arXiv (All)
open access
Estimation and Inference for Latent Markov Models by Fourier Recursions
This paper proposes a new theoretically exact Fourier recursion framework for a broad class of latent Markov models (LMMs), encompassing models widely used across a broad range of fields in economics. It can be viewed as a counterpart of the celebrated Kalman filter for non-Gaussian and nonlinear LM
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
- Time delay estimation with hidden Markov models#234233
- When Recursive Models Finish Computing#1062378
- When Recursive Models Finish Computing#1043102
- Bayesian Inference for Discrete Markov Random Fields Through Coordinate Rescaling#1156264
- On Central Limit Theorems for Additive Functionals of Reversible Ergodic Markov Processes#139158
Record · ID 984318
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