- This exercise is about regularization priors. In the code that generates the data, change order=2 to another value, such as order=5. Then, fit model_p and plot the resulting curve. Repeat this, but now using a prior for beta with sd=100 instead of sd=1 and plot the resulting curve. How are both curves different? Try this out with sd=np.array([10, 0.1, 0.1, 0.1, 0.1]), too.
- Repeat the previous exercise but increase the amount of data to 500 data points.
- Fit a cubic model (order 3), compute WAIC and LOO, plot the results, and compare them with the linear and quadratic models.
- Use pm.sample_posterior_predictive() to rerun the PPC example, but this time, plot the values of y instead of the values of the mean.
- Read and run the posterior predictive example from PyMC3's documentation at https://pymc-devs.github.io/pymc3/notebooks/posterior_predictive.html. Pay special...
Bayesian Analysis with Python - Second Edition
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Bayesian Analysis with Python - Second Edition
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Overview of this book
The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.
The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others.
By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.
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Current Title:
Bayesian Analysis with Python - Second Edition
Table of Contents (11 chapters)
Preface
Free Chapter
Thinking Probabilistically
Programming Probabilistically
Modeling with Linear Regression
Generalizing Linear Models
Model Comparison
Mixture Models
Gaussian Processes
Inference Engines
Where To Go Next?
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