MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models
MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models
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DOI：
10.1609/aaai.v33i01.33017858
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发表时间：
2019-07
影响因子：
1.1
通讯作者：
Mahdi Imani;Seyede Fatemeh Ghoreishi;D. Allaire;U. Braga-Neto
中科院分区：
文献类型：
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作者：
Mahdi Imani;Seyede Fatemeh Ghoreishi;D. Allaire;U. Braga-Neto
非线性状态空间模型在真实世界的动态系统建模中是普遍存在的。序贯蒙特卡罗（SMC）技术，也被称为粒子方法，是这类状态空间模型的参数估计方法的一个众所周知的类别。现有的基于SMC的技术依赖于对参数空间的过度采样，这使得它们的计算对于大型系统或高数据集来说是棘手的。贝叶斯优化技术已被用于快速推理的状态空间模型与棘手的可能性。这些技术的目的是通过一个单一的SMC近似器的参数空间的顺序采样，找到最大的似然函数。具有不同保真度和计算成本的各种SMC逼近器通常可用于基于样本的似然逼近。在本文中，我们提出了一个多保真度贝叶斯优化算法的一般非线性状态空间模型（MFBO-SSM）的推理，使参数和逼近器的同时顺序选择。利用基因调控网络模型的合成基因表达数据和VIX股价指数的真实的数据，通过数值实验验证了算法的准确性和速度.
Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems. Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well-known class of parameter estimation methods for this general class of state-space models. Existing SMC-based techniques rely on excessive sampling of the parameter space, which makes their computation intractable for large systems or tall data sets. Bayesian optimization techniques have been used for fast inference in state-space models with intractable likelihoods. These techniques aim to find the maximum of the likelihood function by sequential sampling of the parameter space through a single SMC approximator. Various SMC approximators with different fidelities and computational costs are often available for sample-based likelihood approximation. In this paper, we propose a multi-fidelity Bayesian optimization algorithm for the inference of general nonlinear state-space models (MFBO-SSM), which enables simultaneous sequential selection of parameters and approximators. The accuracy and speed of the algorithm are demonstrated by numerical experiments using synthetic gene expression data from a gene regulatory network model and real data from the VIX stock price index.