# tinypp
[](https://hex.pm/packages/tinypp)
[](https://hexdocs.pm/tinypp/)
`tinypp` is a tiny Gleam package to do *probabilistic programming* (hence the
name).
It is inspired by the probabilistic programming language
[*Church*](https://web.stanford.edu/~ngoodman/papers/churchUAI08_rev2.pdf)
after reading [this fun paper](https://dl.acm.org/doi/10.1145/3759429.3762631).
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.
```sh
gleam add tinypp
```
```gleam
import gleam/float
import gleam/int
import gleam/io
import tinypp.{pmf, normalize, sample, condition, query}
import tinypp/distribution.{uniform}
pub fn main() -> Nil {
// What is the probability that a die shows a value greater than three if we
// know that the value is even?
let distribution_greater_three = {
let die = uniform([1, 2, 3, 4, 5, 6])
use value <- sample(die)
use <- condition(int.is_even(value))
query(value > 3)
}
let p_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](examples/) folder, you can find more elaborate examples,
including Bayesian linear regression.
## Development
```sh
gleam test # Run the tests
```
