Bayesian Analysis with Python: A Practical Guide to Probabilistic Modeling

Bayesian Analysis with Python Third Edition
	Preface
	Chapter 1 Thinking Probabilistically
		1.1 Statistics, models, and this book’s approach
		1.2 Working with data
		1.3 Bayesian modeling
		1.4 A probability primer for Bayesian practitioners
		1.5 Interpreting probabilities
		1.6 Probabilities, uncertainty, and logic
		1.7 Single-parameter inference
		1.8 How to choose priors
		1.9 Communicating a Bayesian analysis
		1.10 Summary
		1.11 Exercises
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	Chapter 2 Programming Probabilistically
		2.1 Probabilistic programming
		2.2 Summarizing the posterior
		2.3 Posterior-based decisions
		2.4 Gaussians all the way down
		2.5 Posterior predictive checks
		2.6 Robust inferences
		2.7 InferenceData
		2.8 Groups comparison
		2.9 Summary
		2.10 Exercises
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	Chapter 3 Hierarchical Models
		3.1 Sharing information, sharing priors
		3.2 Hierarchical shifts
		3.3 Water quality
		3.4 Shrinkage
		3.5 Hierarchies all the way up
		3.6 Summary
		3.7 Exercises
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	Chapter 4 Modeling with Lines
		4.1 Simple linear regression
		4.2 Linear bikes
		4.3 Generalizing the linear model
		4.4 Counting bikes
		4.5 Robust regression
		4.6 Logistic regression
		4.7 Variable variance
		4.8 Hierarchical linear regression
		4.9 Multiple linear regression
		4.10 Summary
		4.11 Exercises
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	Chapter 5 Comparing Models
		5.1 Posterior predictive checks
		5.2 The balance between simplicity and accuracy
		5.3 Measures of predictive accuracy
		5.4 Calculating predictive accuracy with ArviZ
		5.5 Model averaging
		5.6 Bayes factors
		5.7 Bayes factors and inference
		5.8 Regularizing priors
		5.9 Summary
		5.10 Exercises
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	Chapter 6 Modeling with Bambi
		6.1 One syntax to rule them all
		6.2 The bikes model, Bambi’s version
		6.3 Polynomial regression
		6.4 Splines
		6.5 Distributional models
		6.6 Categorical predictors
		6.7 Interactions
		6.8 Interpreting models with Bambi
		6.9 Variable selection
		6.10 Summary
		6.11 Exercises
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	Chapter 7 Mixture Models
		7.1 Understanding mixture models
		7.2 Finite mixture models
		7.3 The non-identifiability of mixture models
		7.4 How to choose K
		7.5 Zero-Inflated and hurdle models
		7.6 Mixture models and clustering
		7.7 Non-finite mixture model
		7.8 Continuous mixtures
		7.9 Summary
		7.10 Exercises
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	Chapter 8 Gaussian Processes
		8.1 Linear models and non-linear data
		8.2 Modeling functions
		8.3 Multivariate Gaussians and functions
		8.4 Gaussian processes
		8.5 Gaussian process regression
		8.6 Gaussian process regression with PyMC
		8.7 Gaussian process classification
		8.8 Cox processes
		8.9 Regression with spatial autocorrelation
		8.10 Hilbert space GPs
		8.11 Summary
		8.12 Exercises
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	Chapter 9 Bayesian Additive Regression Trees
		9.1 Decision trees
		9.2 BART models
		9.3 Distributional BART models
		9.4 Constant and linear response
		9.5 Choosing the number of trees
		9.6 Summary
		9.7 Exercises
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	Chapter 10 Inference Engines
		10.1 Inference engines
		10.2 The grid method
		10.3 Quadratic method
		10.4 Markovian methods
		10.5 Sequential Monte Carlo
		10.6 Diagnosing the samples
		10.7 Convergence
		10.8 Effective Sample Size (ESS)
		10.9 Monte Carlo standard error
		10.10 Divergences
		10.11 Keep calm and keep trying
		10.12 Summary
		10.13 Exercises
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	Chapter 11 Where to Go Next
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	Bibliography
	Other Books You May Enjoy
	Index