Value Shift

Context

With Value Shift, the Target Mean/Value is generated by shifting the values of each particle (or virtual observation) by the exact same amount. Like MinXEnt, Value Shift generates Soft Evidence. This means that the Target Distribution they supply should be understood like a posterior distribution given evidence set on a “hidden cause”, i.e. evidence on a variable not included in the model.

As such, using MinXEnt or Value Shift is suitable for creating evidence that represents changing levels of measures like customer satisfaction. Unlike setting the price of a product, we cannot directly adjust the satisfaction of all customers to a specific level, as this would imply setting an unrealistic distribution with low or no uncertainty. More realistically, we would have to assume that higher satisfaction is the result of an enhanced product or better service, i.e. a cause from outside the model. Thus, we need to generate the evidence for customer satisfaction as if it were produced by a hidden cause. This also means that MinXEnt and Value Shift will produce a distribution close to the marginal one if the targeted Numerical Evidence is close to the marginal value.

Usage