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BayesiaLabBayesiaLab User GuideEditEdit Costs

Edit Costs

Context

In BayesiaLab, each node is associated with a Cost. This Cost represents the “expenditure” or “effort” required to observe a node, i.e., set evidence on the node.

In some cases, a node’s Cost can be set to the monetary cost of acquiring an observation, e.g., a payment of $50 for performing and processing a Coronavirus test.

In other contexts, Cost may be expressed in relative terms, e.g., for a nurse, it may require three times as much effort to measure a patient’s blood pressure compared to measuring their heart rate. So, the node Blood Pressure would have a Cost of 3, while the node Heart Rate would have a Cost of 1.

While the scale of Cost is arbitrary, the scale is the same for all nodes in a Bayesian network. So, once you start assigning a Cost to one node, e.g., $50 to the Coronavirus node, the Cost of Blood Pressure and Heart Rate must be expressed in dollar terms as well.

By default, unless modified, all nodes are set to a Cost of 1.

Not Observable Nodes

A Cost of 0 has special significance in BayesiaLab. It means the node is “not observable,” so its state or value cannot be established through direct observation. As a result, a not-observable node is never included in the prioritized list of “questions to ask” by the Adaptive Questionnaire.

Motivation

The Cost of a node becomes important in the context of collecting evidence for determining the state of a Target Node.

With a given Bayesian network, BayesiaLab can determine how much information gain a node would provide upon observation. However, prioritizing which observations to gather requires taking Cost into account.

For instance, observing a node at a low cost with the expectation of a modest information gain may be preferable to collecting a highly informative observation at a very high cost. A practical example in the medical context would be prioritizing a $100, traditional X-Ray over a $1,000 high-tech MRI scan.

BayesiaLab formalizes this trade-off between cost and information gain in the Adaptive Questionnaire. The Adaptive Questionnaire sorts the Monitors of nodes in an optimal order with respect to the “pertinence” of the potential observations these nodes represent. We use the term “pertinence” as a shorthand for “information gain in consideration of cost given the current state of knowledge.”

Usage

Given their importance, Costs can be added, accessed, and modified in multiple ways in BayesiaLab:

  • Go to Main Menu > Edit > Edit Costs.
  • Select Graph Panel Context Menu > Edit Cost.
  • Select Node Context Menu > Properties > Cost.
  • Click the Cost indicator in the Status Bar.
  • After starting the Adaptive Questionnaire, click Edit Observation Costs.

All of the above paths lead to the Cost Editor, either for multiple nodes or just for an individual node.

The Cost Editor allows you to manage the Cost of all nodes in your network.

  • To edit the values, simply click into the corresponding cell in the table and overwrite the current value.
  • To set a node to Not Observable, enter a value of 0.

Note that this table is always populated with all nodes in the network, so there is no need to add nodes for specifying Cost. Instead, you only need to modify the nodes’ default value of 1.

Example & Workflow Animation

In addition to the above functions, this animation also shows how to export the defined Costs as a Dictionary.

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