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Bayesian Analysis with Python


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Table of Contents

Table of Contents

  1. Thinking probabilistically
  2. Programming probabilistically
  3. Modeling with Linear Regression
  4. Generalizing Linear Models
  5. Model Comparison
  6. Mixture Models
  7. Gaussian Processes
  8. Inference Engines
  9. Where To Go Next?

About the Author

Osvaldo Martin is a researcher at The National Scientific and Technical Research Council (CONICET), in Argentina. He has worked on structural bioinformatics of protein, glycans, and RNA molecules. He has experience using Markov Chain Monte Carlo methods to simulate molecular systems and loves to use Python to solve data analysis problems. He has taught courses about structural bioinformatics, data science, and Bayesian data analysis. He was also the head of the organizing committee of PyData San Luis (Argentina) 2017. He is one of the core developers of PyMC3 and ArviZ.

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