非定常確率過程を条件として含む確率場のシミュレーション手法について

抄録

The objective of this study is to develop a theoretical framework for conditional random fields (CRFs) which consists of non-stationary stochastic processes. For this purpose, we use the probability density function of Fourier amplitudes and phases in frequency domain. The problem area of CRFs in this study is limited in the estimation of stochastic processes conditioned by realized values of time series. To represent the properties of non-stationary processes, we will introduce group delay time spectra, which are gradient of phase spectra with respect to frequency. Using the style of likelihood method, the conditional probability density functions of Fourier phases are updated by information of group delay time. On this basis, a method to generate numerically the conditional random fields containing non-stationary processes is developed and it is verified through the numerical examples that the method can give reasonable results.