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Multiple Data Sources, Missing and Biased Data

de Angelis, D (University of Cambridge)
Wednesday 21 August 2013, 12:00-12:30

Seminar Room 1, Newton Institute


Inferential methods based on multiple data sources are becoming increasingly common in infectious disease epidemiology, to combine heterogeneous, incomplete and biased evidence. We describe a Bayesian approach to evidence synthesis, highlight its ability to incorporate all available information in a single coherent probabilistic model and discuss current challenges in this area.


[pdf ]


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