# Disintegrate

The disintegrate transformation converts a Hakaru program representing a joint probability distribution into a Hakaru program representing a posterior distribution for a target distribution variable. This transform is equivalent to model conditioning in probability theory, where the known data is provided to the transformed Hakaru model.

Note: The disintegrate transform cannot be used to condition variables of type bool or expressions containing Boolean operators.

## Usage

Before you use the disintegrate transform, your Hakaru program should contain a return statement containing the variables for your known and unknown data. The order of the variables in the return statement is important. The variable for the known data should appear first, followed by the variable representing the unknown data.

You can use the disintegrate transform in the command line by calling:

disintegrate hakaru_program.hk


This command will return a new Hakaru program that contains an anonymous function representing the transformed program. The function argument represents the variable for which you will test different values for your unknown variable.

## Example

Let’s condition a joint probability distribution of two independent random variables that are each drawn from a normal distribution. You can define this model in Hakaru using a program such as:

y <~ normal(0,1)
x <~ normal(θ,1)
return (y,x)


In this program, x and y are the independent variables. The statement return (y,x) states that you want to condition your model to create a posterior model for x using known values for y. If you save this program as hello1.hk, you would call the disintegrate transform on it by running:

disintegrate hello1.hk


Note: The output for disintegrate will be printed in the console. You can easily save this program to a file by redirecting the output to a file by calling disintegrate model1.hk > model2.hk. For this example, we will call our new program hello1_D.hk.

The resulting program renames the known-value variable y (here it is renamed to x2) and creates an anonymous function that, given a value for y, calculates the corresponding value for x:

fn x2 real:
x <~ normal(0, 1)
x7 <~ weight((exp((negate(((x2 - x) ^ 2)) / 2))
/
1
/
sqrt((2 * pi))),
return ())
return x