BITESIZE | Hacking Bayesian Models for Better Performance, with Luke Bornn
Learning Bayesian Statistics
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Innovations in Specifying Priors for Bayesian Models
This chapter explores the complexities of defining prior information in Bayesian models, focusing on standard deviation parameters in multivariate normal distributions. It highlights the challenges of specifying interpretable priors and introduces innovative methods to enhance performance by modifying the joint posterior.
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