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Koca Çingene (talk | contribs) I wrote a general description about the problem. The topic was only about molecular biology whereas adaptive sampling is a general term in statistics. |
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Let f(x) be a function that is to be sampled. For simplicity, let C(x,'''s''') be the cost for sample x given the previous set of samples '''s''' (For simplicity, we can assume that C(x,'''s''') is constant since sampling cost usually does not depend on the previous samples and the sampling input x to the function. In time-critical systems, where the cost for each sample is strongly related to computation time; usually there are other parameters to the function C like the current time...); and G(x, '''s''') be the gain (anti-cost) from sampling the function at x, given the set of previous samples '''s'''. For example, it can be assumed that G(x, '''s''')=0 if x has already been sampled. The sampling problem is then maximizing our cumulative gain minus cumulative cost. Which usually comes down to sampling the function n times until the next sample's estimated/deterministic cost C(x,s) is smaller than the gain G(x,s) of that sample.
== Computational Molecular Biology ==
=== Background ===
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