Glossary N to R

Netica

Glossary   N - R

Nature node:  A nature node in a Bayes net represents some variable of interest.  It may also appear in a decision net in which case it is a variable that cannot be directly controlled by the decision maker (i.e. it is determined by nature).  If a nature node has a functional relationship with its parents, it is called a deterministic node, whereas if the relationship is probabilistic, it is called a chance node.  The characteristic shape for a nature node is an ellipse, or a rectangle with rounded corners.

 

Negative finding:  A negative finding is a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_finding.htm');return false;">finding that some node is definitely not in some particular state.  Compare with = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_negative_finding.htm');return false;">positive finding and = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_likelihood_finding.htm');return false;">likelihood finding.  More Info

 

Net:  In Netica documentation, the word net is used to mean a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Bayes_net.htm');return false;">Bayes net or a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_decision_nets.htm');return false;">decision net.

 

Netica:  Netica is a program created by = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Norsys.htm');return false;">Norsys for working with = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Bayes_net.htm');return false;">Bayes nets and = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_decision_nets.htm');return false;">decision nets.  More Info

 

Netica API:  Netica API (also known as “Netica Programmer’s Library”) is software that you can link with your own programs to achieve much of the functionality of = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Netica_Application.htm');return false;">Netica Application.  It is created by = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Norsys.htm');return false;">Norsys and is designed for working with = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Bayes_net.htm');return false;">Bayes nets and = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_decision_nets.htm');return false;">decision nets.  More Info

  

Netica Application:  Netica Application is the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Netica.htm');return false;">Netica product with an easy-to-use graphical interface for building and working with Bayes nets and = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_decision_nets.htm');return false;">decision nets.  To program Netica, use Netica API instead.

 

Netica-Web:  Netica-Web is a system to deploy your Bayes nets over the internet as a question-answer system. Such a system asks the user questions, or provides a dashboard to enter relevant information, and presents the user with conclusions.  More info

 

No-forgetting links:  If a decision maker remembers the decisions he made at an earlier time, and also the knowledge he had available to him at that time, then in his decision net there will be = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_informational_link.htm');return false;">informational links going from earlier decision nodes and their parents, to later decision nodes.  These are called no-forgetting links.  More Info

 

Node:  A node is a component of a Bayes net or decision net used to represent a variable (i.e. scalar quantity) of interest, and in Netica is usually drawn as a rectangle, rounded rectangle, circle or flattened hexagon.  More Info

 

Node dialog box:  To change or view the properties of a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node.htm');return false;">node, such as its name or the states it has, you use a node dialog box, which you obtain by double-clicking on the node, or = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_select_node.htm');return false;">selecting it and then pressing the enter key.  To change its relation with its parent nodes, you use a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_table_dialog_box.htm');return false;">table dialog box.  More Info

 

Current state:  The current state of a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node_dialog_box.htm');return false;">node dialog box, is the state displayed next to the label “State:”   It may be changed by using the popup menu next to the “States:” label.  The state interval thresholds or = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_state_value.htm');return false;">state value displayed is for the current state.  More Info

 

node name:  The node name text edit box in the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node_dialog_box.htm');return false;">node dialog box looks like = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_graphic_node_name.htm');return false;">this.

 

node title:  The node title text box in the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node_dialog_box.htm');return false;">node dialog box looks like = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_graphic_node_title.htm');return false;">this..                                   

 

states label:  The states label in the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node_dialog_box.htm');return false;">node dialog box looks like = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_graphic_states.htm');return false;">this.

                       

Node relationship:  A node relationship, or node relation for short, is the relationship between a node and its = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_parent_node.htm');return false;">parents.  It may provide the value of the node as a function of its parents’ values, or it may provide a probability distribution for the node depending on its parents’ values.  It is often expressed as a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_CPT.htm');return false;">CPT in which case it can be viewed or edited using the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_table_dialog_box.htm');return false;">table dialog box.  Alternately, it may be expressed as a probabilistic or deterministic = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node_equation.htm');return false;">equation.

 

Non-extreme probability:  A non-extreme probability distribution (also known as “strictly positive”) is one where the probability is never 0.  That means that it is also never 1, and that it has no points of complete “certainty”.

 

Normal distribution:  The normal distribution (also known as “Gaussian distribution), is the most commonly used continuous distribution with infinite = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_support.htm');return false;">support.  More Info

 

Normative theory:  A normative theory does not indicate what agents usually do (which is a descriptive theory) or what agents ought to do (which is a prescriptive theory), but what agents must do if they wish to act optimally in a given situation, where optimally is defined in a particular way with respect to the situation.

 

Norsys:  Norsys Software Corp. is the company which develops = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Netica_Application.htm');return false;">Netica Application and = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Netica_API.htm');return false;">Netica API.  You can get more information about Norsys from their web site at: www.norsys.com.  Questions and comments are very welcome, and may be sent by email.

 

Optimal policy:  The optimal policy (also known as the set of optimal decisions) is the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_policy.htm');return false;">policy which results in the greatest = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_expected_value.htm');return false;">expected value for the sum of the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_utility_node.htm');return false;">utility nodes (or one of those policies if there are more than one which result in the same expected utility).  Finding the optimal policy is sometimes called “solving” a decision net.

 

Outcome:  The outcome is the result of an event, or series of events, that could have turned out in one of several ways.

 

Parameter learning:  Parameter learning is the automatic learning of the specific relationships nodes have with their = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_parent_node.htm');return false;">parents using case data, once it has already been determined which nodes are the parents of each node.  These relationships are usually in the form of = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_conditional_probability.htm');return false;">conditional probabilities, or the parameters of a conditional probability equation.  Compare with = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_structure_learning.htm');return false;">structure learning.  More Info

 

Parent node:  If there is a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_link.htm');return false;">link going from = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node.htm');return false;">node A to node B, then A is said to be a parent node of B.  Some people refer to it as a “direct predecessor”.

 

Path:  A path is a sequence of nodes from a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_net.htm');return false;">net, such that you can get from one node of the sequence to the next node by traversing a link between them (but not necessarily in the direction of the arrow).  Compare with = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_directed_path.htm');return false;">directed path.

 

Poisson process:  A Poisson process is one in which events occur randomly and independent of each other.  The number of events that occur in a fixed time period is given by the Poisson distribution, the time between successive events is given by the exponential distribution, and the time required for the occurrence of a fixed number of events is given by the gamma distribution.

 

Policy:  A policy (also known as a “control law”) is a set of = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_decision_rule.htm');return false;">decision rules, with one for each = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_decision_node.htm');return false;">decision node of a decision net.  When Netica “optimizes decisions” it finds the policy which maximizes the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_expected_value.htm');return false;">expected value of utility.

 

Positive finding:  A positive finding is a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_finding.htm');return false;">finding that some node is definitely in some particular state.  Compare with = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_negative_finding.htm');return false;">negative finding and = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_likelihood_finding.htm');return false;">likelihood finding.  More Info

 

Probabilistic inference:  Probabilistic inference is the process of calculating new = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_belief.htm');return false;">beliefs for a set of variables, given some = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_finding.htm');return false;">findings.  Technically speaking, it is the process of finding a posterior distribution, given a prior distribution, a model and some observations.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_Bayes_net.htm');return false;">Bayes nets do probabilistic inference by = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_belief_updating.htm');return false;">belief updating.

 

Probability density function:  The probability density function (also known as “pdf”), is a function that provides the probability of a continuous probability distribution at each point within the distribution.  It may be integrated over a region to determine the probability of that region.  It is nowhere negative and its integral over the whole distribution is always 1.  The integral of the pdf from negative infinity to x is known as the cumulative density function (cdf).

 

Probability revision:  Probability revision is the process of adjusting the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_CPT.htm');return false;">conditional probability tables of a Bayes net to account for a new = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_case.htm');return false;">case (i.e. set of findings), or more often, for a new set of cases.  It is a form of = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_parameter_learning.htm');return false;">parameter learning, which generally involves learning from cases.  Compare with = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_belief_updating.htm');return false;">belief updating.

 

Prospect:  A prospect is the probability distribution over possible = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_outcome.htm');return false;">outcomes, given a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_policy.htm');return false;">policy and some = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_finding.htm');return false;">findings.

 

Query node:  See Target Node.

 

Relation symbol:  The relation symbol is a green tree structure which looks like this: image\Reln_Symbol.gif  It is used on toolbar buttons to indicate the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node_relation.htm');return false;">relation a node has with its parents.  For example, the image\RelnTOOL.gif button is to view or edit a table, the image\DelRelnTOOL.gif  button removes a node’s table, and the image\DiceRelnTOOL.gif  button with a dice randomizes a table.

 

Reports:  Netica can generate a multitude of text reports, useful in understanding the information in your net.  These reports include:

Report Beliefs, which lists the current beliefs (i.e. posterior probabilities) for nature nodes, and expected utilities for decision nodes.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_beliefs.htm');return false;">example

Report CPT Tables, which lists the node relation as a conditional probability table (= 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_CPT.htm');return false;">CPT) or = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_function_table.htm');return false;">function table.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_CPT.htm');return false;">example

Report Elimination, which lists the order used during compiling.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_elimination.htm');return false;">example

Report Equations, which lists all = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node_equation.htm');return false;">equations of nodes.  Choosing the Horizontal Format option prints them in internal form.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_equations.htm');return false;">example

Report Excel, which hot-links to the node beliefs.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_excel.htm');return false;">example

Report Findings, which lists the = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_finding.htm');return false;">findings (i.e. "case" or "evidence") currently entered, including likelihood ("virtual") findings.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_findings.htm');return false;">example

Report Junction Tree,  gives details of the net compilation process.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_junctiontree.htm');return false;">example

Report List Selected, generates a list of the names of the nodes currently selected.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_listselected.htm');return false;">example

Report Node Sets, whichlists the nodes within the requested set.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_nodesets.htm');return false;">example

Report Network, which gives a summary information on the whole net.  = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_report_whole.htm');return false;">example

Retracted:  Anytime after a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_finding.htm');return false;">finding has been = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_enter_finding.htm');return false;">entered into a node, that finding may be removed, or retracted.  After doing belief updating for the net, it will be as if the finding had never been entered.  More Info

 

Right-click:  To right-click on something, place the mouse pointer ("cursor") over it, then press the right mouse button and choose an item from the menu that comes up.  More Info

 

Root node:  A root node is a = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_node.htm');return false;">node with no = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_parent_node.htm');return false;">parents.  See also = 4 && typeof(BSPSPopupOnMouseOver) == 'function') BSPSPopupOnMouseOver(event);" class="BSSCPopup" onclick="BSSCPopup('X_PU_leaf_node.htm');return false;">leaf node.