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Bayesian Analysis with Python

You're reading from   Bayesian Analysis with Python Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789341652
Length 356 pages
Edition 2nd Edition
Languages
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Author (1):
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Osvaldo Martin Osvaldo Martin
Author Profile Icon Osvaldo Martin
Osvaldo Martin
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Table of Contents (11) Chapters Close

Preface 1. Thinking Probabilistically 2. Programming Probabilistically FREE CHAPTER 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?
10. Other Books You May Enjoy

Exercises

  1. For the example in the Covariance functions and kernels section make sure you understand the relationship between the input data and the generated covariance matrix. Try using other input such as data = np.random.normal(size=4)
  2. Rerun the code generating Figure 7.3 and increase the number of samples obtained from the GP-prior to around 200. In the original figure the number of samples is 2. Which is the range of the generated values?
  3. For the generated plot in the previous exercise. Compute the standard deviation for the values of at each point. Do this in the following form:
    • Visually, just observing the plots
    • Directly from the values generated from stats.multivariate_normal.rvs
    • By inspecting the covariance matrix (if you have doubts go back to exercise 1)

Did the values you get from these 3 methods agree?

  1. Re-run the model model_reg and get new plots but...
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