Many problems we find in science, engineering, and business are of the following form. We have a variable 
and we want to model/predict a variable 
. Importantly, these variables are paired like 
. In the most simple scenario, known as simple linear regression, both and are uni-dimensional continuous random variables. By continuous, we mean a variable represented using real numbers (or floats, if you wish), and using NumPy, you will represent the variables or as one-dimensional arrays. Because this is a very common model, the variables get proper names. We call the variables the dependent, predicted, or outcome variables, and the variables the independent, predictor, or input variables. When 
is a matrix (we have different variables), we have what is known as multiple linear regression. In this and the following chapter, we will explore these and other...
Bayesian Analysis with Python
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Bayesian Analysis with Python
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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.
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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