tinypp

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tinypp is a tiny Gleam package to do probabilistic programming (hence the name). It is inspired by the probabilistic programming language Church after reading this fun paper.

It allows you to program probability distributions with discrete support, making heavy use of Gleam's use-syntax. The main functions are sample, condition and query:

• use x <- sample(distribution): State that x is supposed to follow distribution.
• use <- condition(predicate): State that predicate should hold.
• query(quantity): State that you are interested in the distribution of quantity.

See the example below on how put these together. Read the blog article for some behind the scenes details.

gleam add tinypp

importgleam/floatimportgleam/intimportgleam/ioimporttinypp.{pmf,normalize,sample,condition,query}importtinypp/distribution.{uniform}pubfnmain()->Nil{// What is the probability that a die shows a value greater than three if we
// know that the value is even?
letdistribution_greater_three={letdie=uniform([1,2,3,4,5,6])usevalue<-sample(die)use<-condition(int.is_even(value))query(value>3)}letp_greater_three=pmf(normalize(distribution_greater_three),True)io.println("P(value > 3 | value is even) = "<>float.to_string(p_greater_three))}

In the examples folder, you can find more elaborate examples, including Bayesian linear regression.

Development

gleam test # Run the tests