# 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).

In the context of optimization, you have to select the** MinXEnt** estimation when the *Drivers* do not correspond to *Direct Driver* nodes but rather to their effects (the *Direct Drivers* are hidden parents of the *Drivers* in the model). This is the case, for instance, when a node represents a consumer's *satisfaction* with regard to product attribute, rather than a *measure* of the product attribute itself, e.g. the satisfaction with fuel economy (5-very satisfied, 4-somewhat satisfied,..., 1-not at all satisfied) as opposed to the fuel economy itself (MPG, l/100km).

The **MinXEnt** estimation can also be interpreted as a "realistic" or "easy-to-achieve" distribution, as the "distance" from *original* distribution to the *target* distribution is minimized.

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

In the context of optimization, you have to select the** Binary **estimation when the *Drivers* correspond to *Direct Drivers* nodes. Here, **Soft Evidence** does not represent uncertainty. Rather, it is used to generate continuous values by means of interpolation (compare to membership degrees in Fuzzy Logic). Casually speaking, **Binary Evidenc****e** is a "mix" between **Hard Evidence** and **Soft Evidence.**

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

Similarly to **MinXEnt**,** Value Shift** estimation can be used for optimization when the *Drivers* do not correspond to *Direct Driver* nodes but rather to their effects. With **Value Shift** estimation, the **Target Mean Value** is achieved by shifting the rating of each particle by the same amount. For instance, in a satisfaction survey analysis, this estimation simulates changes on *Hidden Direct Drivers* that have the exact same effect (e.g. +1) on each respondent, regardless of what the original satisfaction level of the respondent was.

**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).