# Contents

# Context

#### Monitor Contextual Menu

# New Feature: Binary and Value Shift Distribution Estimation

Three **Distribution Estimation Methods** are now available for generating probability distributions so that a **Target Mean Value** can be achieved.

**Example**

Let's use the following node *Radio* as an example. The marginal mean value this node's distribution is 0.498.

For the purpose of this example, we now arbitrarily set a **Target Mean Value** of 0.598. Theoretically, a large number of distributions could be generated that all have the desired mean value of 0.598.

The following panels show how the three available estimation methods generate different distributions, which each achieve the same **Target Mean Value** of 0.598.

For **MinXEnt**, the target distribution is chosen such that the **Cross-Entropy** is minimized between the *original* probability distribution and the *target* distribution (which produce the **Marginal Mean Value** and the **Target Mean Value** respectively).

The **Target Mean Value** is generated by interpolating between two adjacent state values.

The **Target Mean Value** is generated by shifting the values of each particle by the exact same amount.

**No Fixing** is the option for setting static likelihood distributions. Once the probability distribution has been found by using one of the three **Distribution Estimation Methods**, the corresponding likelihood distribution is computed and associated to the node.

**Fix Means** and **Fix Probabilities** are the options for setting dynamic likelihood distributions, i.e. distributions that are dynamically updated after each new finding to prevent changes of the target mean values.

**Fix Mean** is only based on the MinXEnt estimation. It allows updating the distribution after each new finding to minimize the cross-entropy between the final distribution and the posterior distribution (the one that takes into account the current pieces of evidence).