Rerun the first model using the variables petal length and then petal width. What are the main differences in the results? How wide or narrow is the 95% HPD interval in each case?
-
Repeat exercise 1, this time using a Student's t-distribution as weakly informative prior. Try different value of
.
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 the previous chapter. How useful is a linear regression compared to the logistic regression? Can the result be interpreted as a probability? Hint: check if the values of y are restricted to the [0, 1] interval.
Suppose instead of a softmax regression we use a simple linear model by coding
setosa =0,versicolor =1, andvirginica = 2. Under the simple linear regression model, what will happen if we switch the coding? Will we get the same, or different, results?In the example for dealing with unbalanced...
Bayesian Analysis with Python
Bayesian Analysis with Python
Overview of this book
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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Bayesian Analysis with Python
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Table of Contents (15 chapters)
Bayesian Analysis with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Thinking Probabilistically - A Bayesian Inference Primer
Programming Probabilistically – A PyMC3 Primer
Juggling with Multi-Parametric and Hierarchical Models
Understanding and Predicting Data with Linear Regression Models
Classifying Outcomes with Logistic Regression
Model Comparison
Mixture Models
Gaussian Processes
Index
Customer Reviews