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Target Optimization — Tree

• This function searches for policies, i.e., sets of evidence, to optimize the Target Node.

• Two options are available:

• Optimization of the specified Target State
• Optimization of the Target Node's mean value
• It is similar to the Target Dynamic Profile except that instead of finding only the "optimal policy" it also tests alternative optimization policies.

• The algorithm finds the best evidence on a node to optimize the target (state or mean). Once this node found, two branches in the tree are created:

• the left branch has the node observed and it stays observed in the subtree
• the right branch has no observation, and this node cannot be observed anymore in this subtree. It is called an alternate tree.
• For each branch, the search is done again on the remaining nodes with the same rules until one of the stop criterions is obtained or no more node is available.

• Filtered States are not taken into account in the search.

• The following panel allows entering all the parameters:

Target

• You can select the target node. By default, the current target node is used. After that, you can choose to optimize the selected target state or the mean.

Optimization Criterion

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

Search Method

• Several search methods are available:
• By Hard Evidence on the states of the nodes.
• 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.
• The percentages of the negative and positive variations of each node can be modified with the mean variation editor.

Search Stop Criteria

• The settings panel allows you to restrict the search with four different stop criterions that can be combined.
• You can specify the maximum number of nodes that can be observed in a branch of the tree.
• You can also specify the maximum number of alternate trees allowed. Alternate trees correspond to the nodes that are skipped in the right branches of the tree.
• The algorithm can stop also when an evidence reaches the minimum joint probability allowed.
• The automatic stop criterion is an heuristic test that allows us to find a good balance between the optimization due to an evidence and the impact on the joint probability.

Output

• The results will be associated as an Evidence Scenario](/bayesialab/user-guide/data/evidence-scenarios).
• Each branch of the tree corresponds to a line of evidence in the Evidence Scenario File](/bayesialab/user-guide/data/evidence-scenarios).
• If an Evidence Scenario File](/bayesialab/user-guide/data/evidence-scenarios)](/bayesialab/user-guide/data/evidence-scenarios) is already associated, you can choose to replace it or to append the examples at the end.

Results

• The results are displayed in a binary tree.

• Each node of the tree represents the node chosen by the algorithm.

• The evidence context will be displayed at the top if necessary.

• A node contains four parts :

• The name or comment of the target node. The background color matches the node's color.
• The probability of the target state of the target node before setting the evidence if we optimize the target state or the mean of the target node before setting the evidence if we optimize the mean.
• The joint probability before setting the evidence.
• If the node is not a leaf, the name or comment of the chosen node that will be observed. The used background is the node's color. Otherwise, it shows the value of the optimization score with a white background. The last node of the "optimal branch" has a thick red border in order to find it quickly.
• Two kinds of branches exist:

• The left branches on which the chosen nodes are observed. If hard evidence is used, the name of the observed state is displayed on the branch. If the mean is observed, the value of the mean of the chosen node is displayed. If this mean corresponds to the negative variation, it is displayed in red, otherwise, if it is the positive variation, it is displayed in blue.
• The right branches represent that the chosen node is not observed and skipped. It won't be used in the corresponding subtree.

Toolbar

• The toolbar at the top of the window allows you:
• to zoom in;
• to zoom out;
• to go back to the default zoom level;
• to fit the tree to the window;
• to display the tree horizontally.

Actions

• The buttons at the bottom of the windows can

• Save an image of the tree
• Print the tree
• Displaying the report.

• Displaying the comment instead of the name of the nodes
• Displaying the long names of the states
• Copying the tree as an image or as html. The html copy reproduces the vertical display as well as the horizontal one. The displayed colors are also used.
• It is possible to perform a manual zoom by clicking and dragging the mouse to select the area that will be zoomed in. The shortcut `Ctrl` + mousewheel performs zooming in and zooming out on the tree centered on the cursor.

• Moving the mouse over a node displays a tooltip containing the chain of evidence done up to that node.

• A double-click on a node closes its subtrees in the window.

• Double-click on it to display them again.

Report

• Depending on the options, the button Report displays an report containing the list of the optimal nodes in the tree with their optimal observations and the corresponding optimization score.

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