Conditional Autoregressive models, known as CAR, are a type of spatial models that measure local dependency of random variables. These models are widely used in disease mapping, urban planning and agriculture studies where data consists of an aggregated measure per areal unit. In this talk we will cover a subclass of CAR models (Intrinsic Autoregressive models, ICAR) and the workflow to implement it using Stan -- a probabilistic programming language.
Sue Marquez is a Manager Data Scientist currently working at The Rockefeller Foundation focusing on . She has previously worked as a data scientist at BuzzFeed and as a Statistical geneticist at Northwell Health in New York. She holds a graduate degree in Statistics from the University of Melbourne.
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