Probabilistic inference models
Probabilistic inference models to address bias, validity and credibility of infectious disease data. This project addresses data management and usage for decision support related to EIDM.
  • This will contribute to the development of reliable models of uncertainty. The chosen approach to uncertainty management is based on probabilistic causal models, such as Bayesian networks with probabilistic inference. The credibility of data can be included in these models, and assessed via calculating risk and biases using various measures of uncertainty.
  • Co-Project Investigators: Svetlana Yanushkevich (University of Calgary)
Networks »Networks
OMNI – Research »OMNI – Research
01 Data Management »01 Data Management
Probabilistic inference models
Svetlana Yanushkevich »Svetlana Yanushkevich
Bayesian analysis »Bayesian analysis
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