# Parameter tuning

## Introduction

Parameter tuning is used to find the value/range of a parameter value which results in a certain probability (or range) for a variable of interest (the hypothesis variable).

##### NOTE

If you want to determine how the probability of the hypothesis variable is affected when the value of parameters in the network are changed, see Sensitivity to parameters.

## Simple

In **Simple** mode, parameter tuning will look at changes in the parameters in the network which result in the probability of the hypothesis state (given any evidence) **P(h|e)** lying between a minimum and maximum value specified in the
**Constraint** section.

##### NOTE

Note that the minimum and maximum constraint values can be equal.

The algorithm works by calculating (for each parameter in the network) the one way sensitivity function for the hypothesis state. It then evaluates the sensitivity function to see if the constraint can be satisfied.

## Difference

In **Difference** mode, parameter tuning works in the same way as in **Single** mode except that instead of a single hypothesis we are interested in the difference between two hypothesis given the evidence **e**, i.e. P(h1|e) - P(h2|e).

##### NOTE

Note that the minimum and maximum constraint values can be equal.

## Ratio

In **Ratio** mode, parameter tuning works in the same way as in **Single** mode except that instead of a single hypothesis we are interested in the ratio between two hypothesis given the evidence **e**, i.e. P(h1|e) / P(h2|e).

##### NOTE

Note that the minimum and maximum constraint values can be equal.