Binary
Context
The Binary algorithm produces the desired value through interpolation, as in Fuzzy Logic. Among the three available methods, it generates distributions that have the lowest Entropy. Using the Binary algorithm for generating a Target Mean/Value would be appropriate if two conditions are met:
- There is no uncertainty regarding the evidence, i.e. we want the evidence to represent a specific numerical value. “No uncertainty” would typically apply in situations in which we want to simulate the effects of nodes that represent variables under our control.
- The desired numerical value is not directly available by setting Hard Evidence. In fact, a distribution produced by the Binary algorithm would coincide with Hard Evidence if the requested Target Value/Mean precisely matched the value of a particular state.
Usage
If we select Binary, the Target Mean/Value is generated by interpolating between values of two adjacent states, hence the name. Here, a “mix” of the values of two states, i.e., JNJ<=−0.009 and JNJ<=−0.002, produces the desired mean of −0.01.
