贝叶斯优化是一种高效的全局优化算法。对于给定优化的目标函数，首先在参数

Bayesian optimization is an efficient global optimization algorithm. For a given optimized objective function, first randomly sample in the parameter space to form a preliminary objective function distribution, and then continuously search for the solution that can maximize or minimize the objective function based on historical information, and iterate continuously until the sampling point is fitted The distribution of is roughly in line with the real objective function.

Bayes optimization is an efficient global optimization algorithm. For a given optimized target function, first random sampling is taken in the parameter space to form a preliminary distribution of the target function, and then according to historical information, the solution that can make the target function maximum or minimum is constantly iterative, until the distribution fitted by the sampling point roughly conforms to the real target function.

Bayesian optimization is an efficient global optimization algorithm. For the given optimized objective function, firstly, random sampling is conducted in the parameter space to form the initial distribution of objective function. Then, according to the historical information, the solution which can make the maximum or minimum of the objective function is searched continuously, and the iteration is continued until the distribution fitting through the sampling point is basically in line with the real objective function.<br>