Population computation and majority inference in test tube

We consider a probabilistic interpretation of the test tube which contains a large amount of DNA strands, and propose a population computation using a number of DNA strands in the test tube and a probabilistic logical inference based on the probabilistic interpretation. Second, in order for the DNA-based learning algorithm [4] to be robust for errors in the data, we implement the weighted majority algorithm [3] on DNA computers, called DNA-based majority algorithm via amplification (DNAMA), which take a strategy of “amplifying” the consistent (correct) DNA strands while the usual weighted majority algorithm decreases the weights of inconsistent ones. We show a theoretical analysis for the mistake bound of the DNA-based majority algorithm via amplification, and imply that the amplification to “double the volumes” of the correct DNA strands in the test tube works well.