1. A kernel regression learning method for calculating an estimation value, by use of a multiple kernel function learning method with training data including a plurality of different types of observation data and evaluation values thereof, the kernel regression learning method comprising the steps of: calculating a similarity matrix for each of the different types of observation data; calculating a graph Laplacian for each of the different types of observation data by use of the corresponding similarity matrix thus calculated; providing an entire graph Laplacian as linear combination of the graph Laplacians with coupling constants; and calculating the coupling constants and a variance of an observation model of the observation data by use of a variational Bayesian method with the graph Laplacian, wherein the observation model is a normal distribution, latent variables for explaining the observation data form a normal distribution, and the coupling constants follow a gamma prior distribution; and wherein at least one of the steps is carried out by a computer device, wherein the observation data is time-series data and wherein each of the different types of observation data includes time-series data sequences obtained by filtering the time-series data through different filters, and wherein the different filters are configured to extract periodic components, to extract trend components and to extract residual components.