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Bayesian Modeling and Probabilistic Programming - Rob Zinkov

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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/

      • Free ML Engineering course: http://mlzoomcamp.com
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