Method and Apparatus for Hypothesis Testing

The field of data analysis is characterized by processes that draw conclusions under uncertainty. Hypothesis testing, a process quite prone to errors originating from data noise and other uncertainties, needs decision-making systems that can adapt and evolve with changing data characteristics to efficiently control error probabilities. Existing approaches often fail in timely identification of relevant data amidst noise and in maintaining a low error probability. They lack adaptability to the continually changing data streams, leading to a fixed threshold that falls short in different scenarios. Additionally, reclassification of falsely identified data samples and effective filtering to lower decision error rates are other facilities rarely found in current systems.

Technology Description

This technology, a decision stream in a hypothesis testing problem, compares a received data stream to a threshold. This threshold is cleverly generated from a noise subset of the data stream, according to certain characteristics of observed data. An important consideration here is the probability distribution of the noise subset, used in conjunction with the data stream characteristics to determine the threshold. This threshold-determination process is adaptive in nature, aimed at maintaining a user-prescribed error probability. What sets this technology apart is the use of a decision state machine within the system. This decision state machine controls how noise characteristics are employed in hypothesis testing, helping to increase the detection rate of relevant data and decrease occurrences of error. Its utility extends to evaluating the decision stream for any falsely classified data samples, reclassifying them appropriately. Moreover, it includes a filtering feature on the decision stream, ensuring a maintained low decision error rate.