- 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...
-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating
Bayesian Analysis with Python
By :
Bayesian Analysis with Python
By:
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.
Table of Contents (11 chapters)
Preface
In Progress
| 0 / 5 sections completed |
0%
Free Chapter
Thinking Probabilistically
In Progress
| 0 / 8 sections completed |
0%
Programming Probabilistically
In Progress
| 0 / 9 sections completed |
0%
Modeling with Linear Regression
In Progress
| 0 / 9 sections completed |
0%
Generalizing Linear Models
In Progress
| 0 / 9 sections completed |
0%
Model Comparison
In Progress
| 0 / 9 sections completed |
0%
Mixture Models
In Progress
| 0 / 7 sections completed |
0%
Gaussian Processes
In Progress
| 0 / 9 sections completed |
0%
Inference Engines
In Progress
| 0 / 7 sections completed |
0%
Where To Go Next?
In Progress
| 0 / 1 sections completed |
0%
Other Books You May Enjoy
In Progress
| 0 / 2 sections completed |
0%
Customer Reviews
