  The minor probability events detection is a crucial problem in Big data. Such
events tend to include rarely occurring phenomenons which should be detected
and monitored carefully. Given the prior probabilities of separate events and
the conditional distributions of observations on the events, the Bayesian
detection can be applied to estimate events behind the observations. It has
been proved that Bayesian detection has the smallest overall testing error in
average sense. However, when detecting an event with very small prior
probability, the conditional Bayesian detection would result in high miss
testing rate. To overcome such a problem, a modified detection approach is
proposed based on Bayesian detection and message importance measure, which can
reduce miss testing rate in conditions of detecting events with minor
probability. The result can help to dig minor probability events in big data.
