学术报告

题      目：Trinomial tree method for G-Expectation and sampling for G-normal random variable after measurement

报  告  人：岳兴业  教授  (邀请人：杨舟 )

                                   苏州大学

时      间：12月22日  10：30-11：30

地     点：数科院东楼401

报告人简介:

        岳兴业教授研究方向为多尺度建模及计算金融。现为苏州大学教授、博士生导师，中国工业与应用数学学会理事，曾任中国科技大学教授。学术访问过普林斯顿大学、宾州州立大学、新加坡国立大学、香港科技大学等机构。   发表学术论文40余篇，主持过国家自然科学基金4项，参加过科技部973计划项目1项。

摘      要：

        Given a 1-D G-normal random variable (RV) X, a trinomial tree method is proposed to approximate the G-Expectation E[φ(X)] for any test function φ. The method is stable and convergent. There is no need to worry about the boundary condition. Furthermore, the whole numerical process actually yields the samples to estimate the expectation E[φ(X)]. As we know, direct sampling for a G-normal RV X is infeasible due to the uncertainty of its distribution. But for a `measurement' E[φ(X)], the sampling is feasible. This is just like an measurement on quantum state: before measurement, the state is uncertain and unknown, after measurement, the state is definite.

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