# Contents

This function allows searching for the tuples that either optimize the probability of the target state (likelihood maximization) or optimize the mean value of the target node.

You can choose to maximize or minimize the criterion. A checkbox allows taking the joint probability of the evidence during the search. In this case, we will optimize the a posteriori (probability of the target state knowing the tuple weighted by the occurrence probability of that tuple). In the same way, the cost of evidence can be taken into account. The context (i.e. the evidence) is taken into account during the analysis.

Several search methods are available:

- By
**hard evidence**on nodes' states. - By
**observation****of****the****value/mean**of the nodes. The observation can be done either by fixing means, i.e. the convergence algorithm will find, at each new observation, a probability distribution to obtain the wanted mean, or by fixing the probabilities, i.e. at startup, the probability distributions corresponding to the wanted means are computed once for all and will not change anymore. The observed means will vary in percentage:- Of the initial
**means**of the nodes - Of the variation
**domains**of the nodes - Of the
**margin****progress**of the nodes, i.e. the difference between the initial mean and the minimum of the domain and the difference between the initial mean and the maximum of the domain.

- Of the initial

The percentages of the negative and positive variations of each node can be modified with the **mean** **variation editor**.

The settings panel allows restricting the search to the selected nodes. The size of the evidence is the size of the tuples we want to obtain.

This is an any-time algorithm. In other words, even if this is an exhaustive search over the tuples, it can be interrupted at any moment by clicking on the red light (at the lower left corner of the graph window) without losing the results. Furthermore, we use a heuristic that allows us to begin the search with the most promising tuples.

You can save the found tuples in an **evidence** **scenario** **file**: just choose the number of examples we want to associate or choose to associate all the obtained combinations. If the search is stopped before the end, we won't obtain the wanted number of examples.

If an evidence file is already associated, you can choose to replace it or to append the examples at the end.

The **filtered states** are not taken into account during the search.

A dialog box indicates the end of the optimization.