.png){kind=icon}
返回
Parameter estimation for partially observed McKean-Vlasov diffusions
.png){kind=icon}
OA
AI
DOI：10.1098/rsos.251918.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this article, we consider likelihood-based estimation of static parameters for a class of partially observed McKean-Vlasov (MV) diffusion process with discrete-time observations over a fixed time interval. In particular, using the framework of (Awadelkarim, Jasra, Ruzayqat 2024 SIAM J. Control Optim. 62, 2664-2694 (doi:10.1137/23M160298X)) we develop a new randomized multilevel Monte Carlo method for estimating the parameters, based upon Markovian stochastic approximation (MSA) methodology. New Markov chain Monte Carlo (MCMC) algorithms for the partially observed MV model are introduced facilitating the application of (Awadelkarim, Jasra, Ruzayqat 2024 SIAM J. Control Optim. 62, 2664-2694 (doi:10.1137/23M160298X)). We prove, under assumptions, that the expectation of our estimator is biased, but with expected small and controllable bias. Our approach is implemented on several examples.
Keyword:
parameter estimation
Markovian stochastic approximation
Mckean-Vlasov stochastic differential equations
multilevel Monte Carlo
期刊
IF:
2.9
论文数:
837
被引数:
1.8W
机构
引用论文
.png){kind=icon}
BAYESIAN STATIC PARAMETER ESTIMATION FOR PARTIALLY OBSERVED DIFFUSIONS VIA MULTILEVEL MONTE CARLO通过多级蒙特卡洛对部分观察到的扩散进行贝叶斯静态参数估计
OAAI
OAAI分享收藏分享收藏分享收藏Existence and uniqueness theorems for solutions of McKean–Vlasov stochastic equationsMcKean–Vlasov随机方程解的存在与唯一性定理OAAI
OAAI分享收藏Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion)离散观察到的扩散过程的精确且计算效率高的基于似然的估计 (带讨论)OAAI
OAAI分享收藏
查看更多内容