Approximate Bayesian inference and model selection for stochastic differential equations (SDEs) (A06)

随机微分方程 (SDE) 的近似贝叶斯推理和模型选择 (A06)

• 批准号: 387054963
• 金额: --
• 依托单位: Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS)
• 依托单位国家: 德国
• 项目类别: Collaborative Research Centres
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/387054963?language=en
• 关键词: Approximate; Bayesian; inference; model; selection

This project is concerned with Bayesian semi-parametric and fully nonparametric approaches for estimating drift functions in systems of stochastic differential equations (SDE) and dynamical point process models. We will develop efficient sequential Monte Carlo approaches and variational Bayesian methods for this problem and study their convergence rates and approximation properties. We will also derive new methods for Bayesian model selection to decide - on the basis of available data - which prior from a given collection can be efficiently used for the SDE problem at hand.

本计画主要研究随机微分方程系统与动态点过程模型中，估计漂移函数的贝氏半参数与完全非参数方法。我们将开发有效的序列蒙特卡罗方法和变分贝叶斯方法，这个问题，并研究其收敛速度和近似性能。我们还将推导出贝叶斯模型选择的新方法，以根据现有数据来决定给定集合中的哪一个先验可以有效地用于手头的问题。