Year: 2009
Type: preprint
Abstract: We will outline novel approaches to derive model invariants for hidden Markov and related models. These approaches are based on a theoretical framework that arises from viewing random processes as elements of the vector space of string functions. Theorems available from that framework then give rise to novel ideas to obtain model invariants for hidden Markov and related models.
Source: arXiv (Cornell University)
Author: Schoenhuth, Alexander
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1675338841