# Sigma

Sigma is a probabilistic programming environment implemented in Julia. You can use it to specify probabilistic models as normal Julia programs, and perform inference.

# Installation

Sigma is built on top of Julia. Sigma currently runs on linux only. Sigma is currently highly unstable and hence not yet in the official Julia Package repository. You can still (try to) install it from a Julia REPL with

`Pkg.clone("https://github.com/zenna/Sigma.jl.git")`

Sigma is then loaded with

`using Sigma`

# Usage

Read the documentation, look at the examples, or see the quick start below.

# Quick Start

First we need to include Sigma

`julia> using Sigma`

Then, we create a uniform distribution `x`

and draw 100 samples from it using `rand`

:

```
julia> x = uniform(0,1)
RandVar{Float64}
julia> rand(x, 100)
100-element Array{Float64,1}:
0.376264
0.492391
...
```

Then we can find the probability that `x^2`

is greater than 0.6:

```
julia> prob(x^2 > 0.6)
[0.225463867187499 0.225463867187499]
```

Then we can introduce an exponentially distributed variable `y`

, and find the probability that `x^2`

is greater than 0.6 under the condition that the sum of `x`

and `y`

is less than 1

```
julia> y = exponential(0.5)
julia> prob(x^2 > 0.6, x + y < 1)
[0.053548951048950494 0.06132144691466614]
```

Then, instead of computing conditional probabilities, we can sample from `x`

under the same condition:

```
julia> rand(x, x + y < 1)
0.04740462764340371
```