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原文連結
論文資訊
- 類型：已發表論文
- 日期：2010
摘要
State-space models provide an important body of techniques for analyzing time series. but their use requires estimating Unobserved states The optimal estimate of the state Is its conditional expectation given the observation histories. and computing this expectation is hard when there are 非線性ities Existing filtering methods, including sequential 蒙地卡羅. tend to be either inaccurate or slow In this paper, we study a 非線性 filter for 非線性/non- Gaussian state-space models. which uses Laplace's method. an asymptotic series expansion, to approximate the state's conditional mean and variance, together with a Gaussian conditional distribution This Laplace Gaussian fillet (LGE) gives fast. recursive, deterministic state estimates, with an error which is set by the 隨機 characteristics of the model and is
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