In order to design the feedback policy rules, we use a technique from the control theory literature known as (exact) model matching; it is a completely parameterized technique, allowing for proper symbolic algorithms to be developed.
While the EnKF is the most commonly used data assimilation technique for history matching, it is rarely used for the purpose of production optimization.
Although adaptive classifier ensembles have been applied for robust face matching, it is often assumed that the proportions of faces captured for target and non-target individuals are balanced, known a priori, and do not change over time.
Radiator does not assume a specific kind of matching — it is up to the developer to implement the aggregability function (responsible for matching and aggregating) using the most appropriate techniques for his specific needs.
With matching, it is possible that not every exposed subject will be matched if there are insufficient suitable unexposed subjects.
In addition to gene matching, it is also necessary to summarize transcript-level expression which is often measured using multiple probes.
