贝叶斯推断下部分线性模型中非参数部分的估计OA

When dealing with the nonparametric component of partial linear models,tradi-tional Gaussian process prior methods,while capable of estimating the nonparametric component,have low computational efficiency and are not suitable for handling high-di-mensional data with large sample sizes.To address this issue,a reparameterized method was employed to interpolate and reconstruct the nonparametric component of partial linear models.New prior distributions were assigned to the parameters obtained after recon-struction.Based on these new priors,Bayesian inference was then utilized to derive the closed-form posterior distributions of the parameters.Numerical simulation results dem-onstrate that the method proposed in this article can effectively reduce computational costs and achieve satisfactory inferential outcomes.