Aiming at the problem that the Bayesian prior density function is difficult to determine, this chapter uses robust data to establish the Bayesian prior density function, and obtains the Bayesian posterior density function according to the prior density function and the distribution law of the experimental data. Therefore, there should be an estimation range and accuracy.

Usually, the posterior density function is first calculated from the prior density function and experimental data, and then the interval of the parameter θ is obtained according to the posterior density function, that is, the Bayesian evaluation interval. However, it is not enough to give only one data, because the experimental data of rolling bearing performance is only an approximation of the sample, so this point estimate is only an approximation of the performance parameters of the rolling bearing.

And then calculate the parameter interval estimation under a certain confidence level according to the Bayesian posterior density function.... Rolling bearing performance point estimation In the rolling bearing performance point estimation, assuming that the estimated parameter Mechanical ball bearing manufacturers is day, an estimate of the unknown parameter θ can be recorded as θ(X1, X2,. In Bayesian evaluation, the posterior density function occupies a very important position. Rolling bearing performance interval estimation assumes that the distribution function of rolling bearing performance is F(X; θ), θ is the quantity to be estimated..

Bayesian interval evaluation of rolling bearing performance: 1. In order to make up for the shortcomings of point estimation in this respect, interval estimation of rolling bearing performance parameters can be used.