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Jul, 2023
将路径相关的NJ-ODEs扩展至有噪声观测和一个相关观测框架
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
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William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann
TL;DR
路径依赖的神经跳跃ODE(PD-NJ-ODE)是一种预测具有不规则和不完整观测的连续时间随机过程的模型。本文讨论了两种扩展方法,以消除对观测时间和噪声的独立假设,并为其提供了理论保证和实证示例。
Abstract
The
path-dependent neural jump ode
(PD-NJ-ODE) is a model for predicting
continuous-time stochastic processes
with
irregular and incomplete obser
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