论文标题
变分和参数估计
Variational State and Parameter Estimation
论文作者
论文摘要
本文考虑了计算非线性状态空间模型的两种状态和模型参数的贝叶斯估计的问题。通常，此问题没有可牵引的解决方案，必须使用近似值。在这项工作中，使用各种方法来提供假定的密度，该密度近似于所需的，棘手的分布。该方法是确定性的，导致标准形式的优化问题。由于假定的密度所选的一阶和二阶导数的参数化，因此很容易获得有效的解决方案。在两个数值示例中，将提出的方法与最新的哈密顿蒙特卡洛进行了比较。
This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tractable solution and approximations must be utilised. In this work, a variational approach is used to provide an assumed density which approximates the desired, intractable, distribution. The approach is deterministic and results in an optimisation problem of a standard form. Due to the parametrisation of the assumed density selected first- and second-order derivatives are readily available which allows for efficient solutions. The proposed method is compared against state-of-the-art Hamiltonian Monte Carlo in two numerical examples.
