Application of Composite Kernel Function in Gaussian Process Prediction

Abstract

Gaussian Process is a non-parametric Bayesian method based on kernel functions, widely used in regression tasks and Bayesian optimization. Kernel functions play a crucial role in Gaussian processes as they directly impact the model's ability to fit the data distribution. Different kernel functions can be selected based on the characteristics of the data to improve model performance. In practical applications, combining multiple basic kernel functions can construct more complex kernel functions, enhancing the flexibility and adaptability of Gaussian processes, making them better suited to complex data modeling challenges. This paper uses California housing price data as an example to analyze the impact of different kernel function combinations on model prediction performance. The experimental results validate the importance of kernel selection in Gaussian process models and demonstrate the effectiveness of composite kernels in handling complex data distributions.