Stochastic Modelling, Big Data and Deep Learning

主講人：毛學(xué)榮 英國Strathclyde大學(xué)數(shù)學(xué)與統(tǒng)計(jì)系教授

時(shí)間：2024年6月21日14：00

地點(diǎn)：三號樓332室

舉辦單位：數(shù)理學(xué)院

主講人介紹：毛學(xué)榮，英國Strathclyde大學(xué)數(shù)學(xué)與統(tǒng)計(jì)系教授、1969統(tǒng)計(jì)學(xué)首席教授、愛丁堡皇家學(xué)會(huì)（即蘇格蘭皇家學(xué)院）院士、“英國沃弗森研究功勛獎(jiǎng)”獲得者。

內(nèi)容介紹：In this talk we will explain how the ordinary differential equations (ODEs) are not enough to model the underlying stochastic quantity and why stochastic differential equations (SDEs) appear naturally. Several well-known SDE models will be presented including the Nobel prize winning model in finance, stochastic SIS epidemic model, stochastic Lotka-Volterra model in population dynamics. We will then explain how SDE models differ significantly from ODE models and reveal the crucial role of noise. We will then emphasise that the use of SDE models depend on the estimation of system parameters. In the case when the model has only a few parameters, we show how they can be estimated by the classical statistical methods, e.g., the least-square method; while when there are lots of parameters, we will show how the deep learning plays its crucial role.