Meaning
A data collection method adjusts the frequency or location of observations based on the values of the initial measurements. This process of adaptive sampling concentrates observation hours where the rate of change is highest, reducing the need for continuous measurement across a uniform grid. The method applies to feedback loops where variance is non-uniform across the observed domain.
Allocation Rule
An algorithm determines where to deploy the next measurement based on the local gradient of the collected data. In adaptive sampling, a low variance zone receives fewer observations, while areas showing high volatility receive a higher density of data points. This selective focus prevents the exhaustion of observation capacity on stable regions.
Efficiency Gain
The reduction in the total number of observations required to achieve a target precision offers a direct advantage to the researcher. By using adaptive sampling, a founder reduces the cost of continuous testing without losing the resolution of critical events. This shift in resource distribution yields a clearer signal with fewer data points.
Boundary Condition
The presence of a delay in processing initial results limits the utility of dynamic adjustment. When the feedback loop is slow, adaptive sampling fails because the allocation rule relies on outdated measurements, leading to incorrect sampling densities in rapidly shifting areas. Under these conditions, a static random design provides a more reliable output, as it does not suffer from latency in the decision cycle.
Thus, the technique works best when processing times are negligible compared to the rate of change in the observed variable.
