Black-Box Inference for Non-Linear Latent Force Models
Black-Box Inference for Non-Linear Latent Force Models
复制标题
非线性潜在力模型的黑盒推理
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Mauricio A Álvarez
中科院分区：
文献类型：
--
作者：
W. Ward;Tom Ryder;D. Prangle;Mauricio A Álvarez
潜力模型是一个描述系统状态动态的机械模型，其中一些未知的强迫项近似于高斯过程。如果这种动态是非线性的，则可能难以联合估计后验状态和强迫项，特别是当存在也需要估计的系统参数时。本文使用黑盒变分推理来联合估计后验，设计了一个多变量扩展到局部逆自回归流作为系统的一个灵活的近似。我们比较估计系统的后验是已知的，证明了近似的有效性，并适用于非线性动态，多输出系统和模型与非高斯似然问题。
Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussian process. If such dynamics are non-linear, it can be difficult to estimate the posterior state and forcing term jointly, particularly when there are system parameters that also need estimating. This paper uses black-box variational inference to jointly estimate the posterior, designing a multivariate extension to local inverse autoregressive flows as a flexible approximater of the system. We compare estimates on systems where the posterior is known, demonstrating the effectiveness of the approximation, and apply to problems with non-linear dynamics, multi-output systems and models with non-Gaussian likelihoods.