On the total variation distance of labelled Markov chains
- Submitting institution
-
The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9027862_1
- Type
- E - Conference contribution
- DOI
-
10.1145/2603088.2603099
- Title of conference / published proceedings
- Proceedings of the Joint Meeting of the Twenty-Third EACSL Annual Conference on Computer Science Logic (CSL) and the Twenty-Ninth Annual ACM/IEEE Symposium on Logic in Computer Science (LICS) - CSL-LICS '14
- First page
- 1
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- -
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper considers a widely-studied distance measure of probability distributions on Markov chains where previous studies focused on distributions restricted on finite paths. The paper is significant because it is the first to consider the distance of probability measures on an uncountable set of infinite paths, which is more accurate in system modelling/verification, and the associated algorithmic questions. This study has impacted on further research in, e.g., approximation of Markov chains (Abate-Oxford), variants of distances (Larsen-Aalborg, Akshay-IIT Bombay), and applications in diagnoses (Bertrand-Inria) and differential privacy (Chistikov-Warwick).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -