𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Optimal filters for a hidden Markov random field model

✍ Scribed by L. Aggoun; L. Benkherouf; A. Benmerzouga

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
550 KB
Volume
31
Category
Article
ISSN
0895-7177

No coin nor oath required. For personal study only.

✦ Synopsis

Markov random field (MRF) is a useful technical tool for modeling dynamics systen~s exhibiting some type of spatio-temporal variability.

In this paper, we propose optimal filters for the states of a partially observed temporal Markov random field. We also discuss parameters estimation. This generalizes an earlier work by Elliott and Aggoun [I].

πŸ“œ SIMILAR VOLUMES

A Markov random field model for bony tis
A Markov random field model for bony tissue classification
✍ J.M. Pardo-LΓ³pez; D. Cabello; J. Heras; J. Couceiro πŸ“‚ Article πŸ“… 1998 πŸ› Elsevier Science 🌐 English βš– 863 KB

3D biomedical images are a valuable source of information for clinical diagnosis. In areas such as bone remodeling, fracture prediction and prosthesis design, the external geometry of the bones needs to be precisely defined and injuries identified. A system that automatically interprets and presents

Analysis of an optimal hidden Markov mod
Analysis of an optimal hidden Markov model for secondary structure prediction
✍ Juliette Martin; Jean-FranΓ§ois Gibrat; FranΓ§ois Rodolphe πŸ“‚ Article πŸ“… 2006 πŸ› BioMed Central 🌐 English βš– 794 KB

## Background Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are grap