What are some best practices for writing efficient and readable Stan code?

1 Use functions and comments

One of the best ways to make your Stan code more readable and reusable is to use functions and comments. Functions allow you to encapsulate complex or repetitive operations, such as transformations, priors, or likelihoods, and give them meaningful names. Comments help you document the logic and assumptions behind your code, and make it easier for others to understand and modify it. You can define your own functions in the functions block of your Stan program, or use the built-in functions provided by Stan. You can also comment your code using // for single-line comments or /* and */ for multi-line comments.

2 Vectorize your operations

Another way to improve the efficiency and readability of your Stan code is to vectorize your operations whenever possible. Vectorization means applying an operation to a whole vector or matrix at once, rather than looping over its elements. Vectorization can speed up your code by taking advantage of the optimized matrix algebra libraries used by Stan, and reduce the amount of code you need to write. For example, instead of writing a for loop to calculate the log-likelihood of a normal distribution for each observation, you can use the vectorized normal_lpdf function that takes a vector of observations and a vector of parameters as inputs.

3 Use informative priors

A key aspect of Bayesian inference is the use of prior distributions to express your beliefs and uncertainties about the parameters of your model. However, choosing appropriate priors can be tricky, and using vague or improper priors can lead to biased or unreliable estimates. Therefore, it is advisable to use informative priors that reflect your domain knowledge and data characteristics, and avoid priors that are too flat, too narrow, or too extreme. You can use the prior predictive checks feature of Stan to visualize and evaluate the implications of your priors before fitting your model.

4 Test and debug your code

Before running your Stan model on your full data set, it is important to test and debug your code to ensure that it works as expected and does not contain any errors or bugs. You can use the print or reject statements to display messages or stop the execution of your code if certain conditions are not met. You can also use the generated quantities block to calculate and output diagnostic quantities, such as posterior predictive checks, log-likelihoods, or residuals. You can use the cmdstanr or rstan packages in R, or the cmdstanpy or pystan packages in Python, to interface with Stan and run your code.

5 Optimize your model

Once you have verified that your Stan code is correct and runs without errors, you may want to optimize your model to make it faster and more efficient. There are several ways to do that, such as using non-centered parameterizations, reducing unnecessary computations, or choosing suitable algorithms and settings for sampling or optimization. You can use the summary or diagnose functions in Stan to check the quality of your results, such as the effective sample size, the R-hat statistic, or the divergence rate. You can also use the profiling feature of Stan to measure the execution time of different parts of your code and identify potential bottlenecks.