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Bayesian Analysis with Python. Click here to enter text

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Bayesian Analysis with Python. Click here to enter text Osvaldo Martin - okladka książki

Bayesian Analysis with Python. Click here to enter text Osvaldo Martin - okladka książki

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The purpose of this book is to teach the main concepts of Bayesian data analysis. We will learn how to effectively use PyMC3, a Python library for probabilistic programming, to perform Bayesian parameter estimation, to check models and validate them. This book begins presenting the key concepts of the Bayesian framework and the main advantages of this approach from a practical point of view. Moving on, we will explore the power and flexibility of generalized linear models and how to adapt them to a wide array of problems, including regression and classification. We will also look into mixture models and clustering data, and we will finish with advanced topics like non-parametrics models and Gaussian processes. With the help of Python and PyMC3 you will learn to implement, check and expand Bayesian models to solve data analysis problems.

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O autorze książki

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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