# Updating priors

by: Kevin Broløs and Meera Machado

(Feyn version 3.0 or newer)

We can change the behaviour of how we sample `Model`

s from a `QLattice`

by supplying **prior probabilities** for each input.

This can be used to inform a `QLattice`

of our prior beliefs of the input variables. Inputs with higher **prior** values are more likely to appear in the sampled `Model`

s.

In short, a prior probability of an input `x`

in the context of a `QLattice`

denotes our prior belief of the importance of `x`

in predicting the output.

If no priors are given, all inputs are equally likely to appear when sampling a `Model`

.

## Example

Here's an example of how to update the priors:

```
import feyn
priors = {"x1": 1., "x2": 0.99, "x3": 0.98, "x4": 0.97}
ql = feyn.QLattice()
ql.update_priors(priors, reset=True)
```

**priors**

Calculating the Refer to Estimating priors to see how the **priors** can be calculated based on the mutual information between each input variable and the output.

`update_priors`

Parameters of ### priors

A `Dict`

object where the `keys`

are the names of the input variables and the `values`

are the relative weights associated to each input.

### reset

Default: True

A boolean that determines whether the **existing priors** should be reset (**True**) or merged with the **new priors** (**False**) when updating the `QLattice`

.