This paper presents a statistical machine learning method that generates the implied volatility surface under the rareness of the market data. We apply the practitioner's Black-Scholes model and Gaussian process regression method to construct a Bayesian inference system with observed volatilities as a prior information and estimate the posterior distribution of the unobserved volatilities. The variance instead of the volatility is the target of the estimation, and the radial basis function is applied to the mean and kernel function of the Gaussian process regression. We present two types of Gaussian process regression methods and empirically analyze them.

This paper aims to examine how the public perception of mathematics changed in England in the 19th century. As rapid industrial and social developments took place in the 19th century, the educational environment underwent great changes, and the value and public perception of mathematics also changed. Mathematics took a new position in the terrain of educational reform in the late 19th century. In this study, I analyzed the actual condition of mathematics education in elementary and secondary schools, popular educational institutions, and universities in England in the first half, middle, and second half of the 19th century, and compared what values and usefulness of mathematics education were justified in each institution. I also examined how satirical magazine Punch satirized the public understanding or view on mathematics at each period. It is to be hoped that this study will have significant implications for raising the public's positive perception of mathematics in modern society.