Approximating Markov Processes by Averaging
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 64025583
- Type
- D - Journal article
- DOI
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10.1145/2537948
- Title of journal
- Journal of the ACM
- Article number
- 5
- First page
- -
- Volume
- 61
- Issue
- 1
- ISSN
- 0004-5411
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- 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
-
3
- Research group(s)
-
C - Foundations of Computation
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article defines an approximate semantics of Markov chains based on a generalised notion of conditional expectation. We show convergence of approximants in the Stone space of the Desharnais-Panangaden bisimulation logic, and in later work, in the Strong operator topology. We obtain a general adjunction result between sigma-continuous linear operators on Selinger's complete normed cones of type Lp(μ) for 1 ≤ p ≤ ∞. This central result has led to a general form of Bayesian inversion with no topological conditions on the data space and which commutes to approximation and thus can operate on approximate versions of the model.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -