prior probability
1.In the process of recognition, the prior probability is supposed to be the same, the posterior probability is calculated according to GMM, and then the instrument class is determined.
2.The Bayes Risk Theorem is also deduced, the genuine application value is pointed out. In addition, the calculation of the real mean risk value, the mean test number and prior probability P_H_0 are discussed.
3.Compared with the regular rule-based expert system, the Bayesian network based ES can reason on the incomplete input information using the prior probability distribution; the topological structure of the network being used to express the qualitative knowledge and the probability distributions of the nodes in the network being used to express the uncertainty of the knowledge, which made the knowledge representation more intuitively and more clearly; applying the principle of the Bayesian chaining rule, bidirectional inference which allow infer from the cause to the effect and from the effect to the cause can be achieved.
