---
title: "Distributions: Key Functions"
sidebar_position: 3
---
import { SquiggleEditor } from "../../src/components/SquiggleEditor";
## Operating on distributions
Here are the ways we combine distributions.
### Addition
A horizontal right shift. The addition operation represents the distribution of the sum of
the value of one random sample chosen from the first distribution and the value one random sample
chosen from the second distribution.
### Subtraction
A horizontal left shift. A horizontal right shift. The substraction operation represents
the distribution of the value of one random sample chosen from the first distribution minus
the value of one random sample chosen from the second distribution.
### Multiplication
A proportional scaling. The addition operation represents the distribution of the multiplication of
the value of one random sample chosen from the first distribution times the value one random sample
chosen from the second distribution.
We also provide concatenation of two distributions as a syntax sugar for `*`
### Division
A proportional scaling (normally a shrinking if the second distribution has values higher than 1).
The addition operation represents the distribution of the division of
the value of one random sample chosen from the first distribution over the value one random sample
chosen from the second distribution. If the second distribution has some values near zero, it
tends to be particularly unstable.
### Exponentiation
A projection over a contracted x-axis. The exponentiation operation represents the distribution of
the exponentiation of the value of one random sample chosen from the first distribution to the power of
the value one random sample chosen from the second distribution.
### Taking the base `e` exponential
### Taking logarithms
A projection over a stretched x-axis.
Base `x`
#### Validity
- `x` must be a scalar
- See [the current discourse](https://github.com/quantified-uncertainty/squiggle/issues/304)
### Pointwise addition
For every point on the x-axis, operate the corresponding points in the y axis of the pdf.
**Pointwise operations are done with `PointSetDist` internals rather than `SampleSetDist` internals**.
TODO: this isn't in the new interpreter/parser yet.
### Pointwise subtraction
TODO: this isn't in the new interpreter/parser yet.
### Pointwise multiplication
### Pointwise division
### Pointwise exponentiation
## Standard functions on distributions
### Probability density function
The `pdf(dist, x)` function returns the density of a distribution at the
given point x.
#### Validity
- `x` must be a scalar
- `dist` must be a distribution
### Cumulative density function
The `cdf(dist, x)` gives the cumulative probability of the distribution
or all values lower than x. It is the inverse of `quantile`.
#### Validity
- `x` must be a scalar
- `dist` must be a distribution
### Quantile
The `quantile(dist, prob)` gives the value x for which the sum of the probability for all values
lower than x is equal to prob. It is the inverse of `cdf`. In the literature, it
is also known as the quantiles function. In the optional `summary stistics` panel which appears
beneath distributions, the numbers beneath 5%, 10%, 25% etc are the quantiles of that distribution
for those precentage values.
#### Validity
- `prob` must be a scalar (please only put it in `(0,1)`)
- `dist` must be a distribution
### Mean
The `mean(distribution)` function gives the mean (expected value) of a distribution.
### Sampling a distribution
The `sample(distribution)` samples a given distribution.
## Converting between distribution formats
Recall the [three formats of distributions](/docs/Discussions/Three-Formats-Of-Distributions). We can force any distribution into `SampleSet` format
Or `PointSet` format
#### Validity
- Second argument to `SampleSet.fromDist` must be a number.
## Normalization
Some distribution operations (like horizontal shift) return an unnormalized distriibution.
We provide a `normalize` function
#### Validity - Input to `normalize` must be a dist
We provide a predicate `isNormalized`, for when we have simple control flow
#### Validity
- Input to `isNormalized` must be a dist
## `inspect`
You may like to debug by right clicking your browser and using the _inspect_ functionality on the webpage, and viewing the _console_ tab. Then, wrap your squiggle output with `inspect` to log an internal representation.
Save for a logging side effect, `inspect` does nothing to input and returns it.
## Truncate
You can cut off from the left
You can cut off from the right
You can cut off from both sides