Bayesian Modeling and Probabilistic Programming - Rob Zinkov

We talked about:

• Rob’s background
• Going from software engineering to Bayesian modeling
• Frequentist vs Bayesian modeling approach
• About integrals
• Probabilistic programming and samplers
• MCMC and Hakaru
• Language vs library
• Encoding dependencies and relationships into a model
• Stan, HMC (Hamiltonian Monte Carlo) , and NUTS
• Sources for learning about Bayesian modeling
• Reaching out to Rob

Links:

• Book 1: https://bayesiancomputationbook.com/welcome.html
• Book/Course: https://xcelab.net/rm/statistical-rethinking/

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