In this LBAM, we’ll introduce the microbiological task of cell counting and understand all the potential sources of error involved. We’ll model each source of error probabilistically, introduce priors, and then discuss inference on the posterior. Finally, we’ll explore how we can extend our model to use in a calibration curve for other instruments. Only basic probability theory is required for this LBAM.
Cameron Davidson-Pilon has worked in many areas of applied statistics, from the evolutionary dynamics of genes to modeling of financial prices. His contributions to the community include lifelines, an implementation of survival analysis in Python, lifetimes, and Bayesian Methods for Hackers, an open source book & printed book on Bayesian analysis. Formally Director of Data Science at Shopify, Cameron is now applying data science to food microbiology.
This is a PyMCon 2020 talk
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Speaker Tag: @CamDavidsonPilon