Stan User's Guide
Overview
Part 1. Example Models
1 Regression Models
2 Time-Series Models
3 Missing Data and Partially Known Parameters
4 Truncated or Censored Data
5 Finite Mixtures
6 Measurement Error and Meta-Analysis
- 6.1 Bayesian Measurement Error Model
  - Regression with Measurement Error
  - Rounding
- 6.2 Meta-Analysis
  - Treatment Effects in Controlled Studies
7 Latent Discrete Parameters
8 Sparse and Ragged Data Structures
- 8.1 Sparse Data Structures
- 8.2 Ragged Data Structures
9 Clustering Models
10 Gaussian Processes
11 Directions, Rotations, and Hyperspheres
12 Solving Algebraic Equations
13 Ordinary Differential Equations
14 Computing One Dimensional Integrals
- 14.1 Calling the Integrator
  - 14.1.1 Limits of Integration
  - 14.1.2 Data Versus Parameters
- 14.2 Integrator Convergence
  - 14.2.1 Zero-crossing Integrals
  - 14.2.2 Avoiding precision loss near limits of integration in definite integrals
Part 2. Programming Techniques
15 Floating Point Arithmetic
16 Matrices, Vectors, and Arrays
17 Multiple Indexing and Range Indexing
18 User-Defined Functions
19 Custom Probability Functions
- 19.1 Examples
20 Problematic Posteriors
21 Reparameterization and Change of Variables
22 Efficiency Tuning
23 Parallelization
- 23.1 Reduce-Sum
  - 23.1.1 Example: Logistic Regression
  - 23.1.2 Picking the Grainsize
- 23.2 Map-Rect
Part 3. Posterior Inference & Model Checking
24 Posterior Predictive Sampling
25 Simulation-Based Calibration
26 Posterior and Prior Predictive Checks
27 Held-Out Evaluation and Cross-Validation
28 Poststratification
29 Decision Analysis
30 The Bootstrap and Bagging
Appendices
31 Stan Program Style Guide
32 Transitioning from BUGS
References

Stan User’s Guide

This is an old version, view current version.

Appendices

These are the appendices for the book, including a program style guide and a guide to translating BUGS or JAGS programs.