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You're reading from  Bayesian Analysis with Python. - Second Edition

Product typeBook
Published inDec 2018
Reading LevelIntermediate
PublisherPackt
ISBN-139781789341652
Edition2nd Edition
Languages
Python
Concepts
Data Science
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Author (1)
Osvaldo Martin
Osvaldo Martin
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Osvaldo Martin

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.
Read more about Osvaldo Martin

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Exercises

  1. Rerun the first model using the petal length and then petal width variables. What are the main differences in the results? How wide or narrow is the 95% HPD interval in each case?
  2. Repeat exercise 1, this time using a Student's t-distribution as a weakly informative prior. Try different values of .
  3. Go back to the first example, the logistic regression for classifying setosa or versicolor given sepal length. Try to solve the same problem using a simple linear regression model, as we saw in Chapter 3, Modeling with Linear Regression. How useful is linear regression compared to logistic regression? Can the result be interpreted as a probability? Tip, check whether the values of are restricted to the [0, 1] interval.
  1. In the example from the Interpreting the coefficients of a logistic regression section, we changed sepal_length by 1 unit. Using Figure 4.6, corroborate...
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Bayesian Analysis with Python. - Second Edition
Published in: Dec 2018Publisher: PacktISBN-13: 9781789341652
© 2018 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Osvaldo Martin

Osvaldo Martin is a researcher at CONICET, in Argentina. He has experience using Markov Chain Monte Carlo methods to simulate molecules and perform Bayesian inference. He loves to use Python to solve data analysis problems. He is especially motivated by the development and implementation of software tools for Bayesian statistics and probabilistic modeling. He is an open-source developer, and he contributes to Python libraries like PyMC, ArviZ and Bambi among others. He is interested in all aspects of the Bayesian workflow, including numerical methods for inference, diagnosis of sampling, evaluation and criticism of models, comparison of models and presentation of results.
Read more about Osvaldo Martin

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