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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