Model Building Insights
Connor explains the utility of Markov chain Monte Carlo in dealing with unnormalized posterior probability distributions, offering a tool to approximate full distributions. The challenge lies in determining when the chain has run long enough for accurate results.In this clip
From this podcast
Machine Learning Street Talk (MLST)
#037 - Tour De Bayesian with Connor Tann
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