Binary hypothesis testing is a classic problem in statistics. Classically, the goal of the problem is to minimize the probability of error as a function of the number of samples. In the finite memory version of the problem, samples are plentiful, but we have only a small amount of memory to work with. This is modeled as the tester being a finite automaton. In a previous talk, we have seen the result for the case when the automaton is randomised. In this talk, we will consider the deterministic case. Although tight results are not known in this case, we can show that deterministic automatons are weaker than their randomised counterparts.
