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 Map-Reduce
Appendices
24 Stan Program Style Guide
25 Transitioning from BUGS
References

Stan User’s Guide

This is an old version, view current version.

Appendices

These are the appendices for the book, gathered here as they are not part of the main exposition.