更新时间：2021-08-20 10:14:06
coverpage
Title Page
Copyright and Credits
Bayesian Analysis with Python Second Edition
Dedication
About Packt
Why subscribe?
Packt.com
Foreword
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Thinking Probabilistically
Statistics models and this book's approach
Working with data
Bayesian modeling
Probability theory
Interpreting probabilities
Defining probabilities
Probability distributions
Independently and identically distributed variables
Bayes' theorem
Single-parameter inference
The coin-flipping problem
The general model
Choosing the likelihood
Choosing the prior
Getting the posterior
Computing and plotting the posterior
The influence of the prior and how to choose one
Communicating a Bayesian analysis
Model notation and visualization
Summarizing the posterior
Highest-posterior density
Posterior predictive checks
Summary
Exercises
Programming Probabilistically
Probabilistic programming
PyMC3 primer
Flipping coins the PyMC3 way
Model specification
Pushing the inference button
Posterior-based decisions
ROPE
Loss functions
Gaussians all the way down
Gaussian inferences
Robust inferences
Student's t-distribution
Groups comparison
Cohen's d
Probability of superiority
The tips dataset
Hierarchical models
Shrinkage
One more example
Modeling with Linear Regression
Simple linear regression
The machine learning connection
The core of the linear regression models
Linear models and high autocorrelation
Modifying the data before running
Interpreting and visualizing the posterior
Pearson correlation coefficient
Pearson coefficient from a multivariate Gaussian
Robust linear regression
Hierarchical linear regression
Correlation causation and the messiness of life
Polynomial regression
Interpreting the parameters of a polynomial regression
Polynomial regression – the ultimate model?
Multiple linear regression
Confounding variables and redundant variables
Multicollinearity or when the correlation is too high
Masking effect variables
Adding interactions
Variable variance
Generalizing Linear Models
Generalized linear models
Logistic regression
The logistic model
The Iris dataset
The logistic model applied to the iris dataset
Multiple logistic regression
