Bayesian modeling of bacterial growth for multiple populations
- Submitting institution
-
University of Greenwich
- Unit of assessment
- 12 - Engineering
- Output identifier
- 16956
- Type
- D - Journal article
- DOI
-
10.1214/14-AOAS720
- Title of journal
- The Annals of Applied Statistics
- Article number
- -
- First page
- 1516
- Volume
- 8
- Issue
- 3
- ISSN
- 1932-6157
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- 10 - Mathematical Sciences
- 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)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Bacterial growth prediction is of high relevance given its implications in subjects such as epidemiology, microbial risk assessment, food safety, etc. In this paper we show a methodological contribution which can be easily and directly applied for microbiological researchers. We propose the use of Bayesian hierarchical neural networks. The proposed models and the Bayesian framework are applicable to a large variety of organism types and under a large number of combination of environmental and ecological variables. The models yield accurate estimations and good predictions.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
