.png){kind=icon}
.png){kind=icon}
Return
A numerically efficient implementation of the expectation maximization algorithm for state space models
.png){kind=icon}
OA
AI
DOI：10.1016/j.amc.2014.05.021.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Empirical time series are subject to observational noise. Naive approaches that estimate parameters in stochastic models for such time series are likely to fail due to the error-in-variables challenge. State space models (SSM) explicitly include observational noise. Applying the expectation maximization (EM) algorithm together with the Kalman filter constitute a robust iterative procedure to estimate model parameters in the SSM as well as an approach to denoise the signal. The EM algorithm provides maximum likelihood parameter estimates at convergence. The drawback of this approach is its high computational demand. Here, we present an optimized implementation and demonstrate its superior performance to naive algorithms or implementations. (C) 2014 Elsevier Inc. All rights reserved.
Keywords:
Kalman filter
Expectation-maximization algorithm
Parameter estimation
State-space model
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.4
Papers:
2.3W
Citations:
3.3W