A BAYESIAN APPROACH TO PROBLEMS IN STOCHASTIC ESTIMATION AND CONTROL
A BAYESIAN APPROACH TO PROBLEMS IN STOCHASTIC ESTIMATION AND CONTROL
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DOI：
10.1109/tac.1964.1105763
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发表时间：
1964-01-01
影响因子：
6.8
通讯作者：
LEE, RCK
中科院分区：
文献类型：
--
作者：
HO, YC;LEE, RCK
本文从贝叶斯决策理论的观点出发，研究了一类随机估计与控制问题。并从原则和实践上讨论了如何从这一途径逐步解决这些问题。作为一个具体的例子，推导了高斯噪声中线性估计的Wiener-Kalman封闭解。本文的目的是表明，贝叶斯方法提供了：1）一个一般的统一框架内进行进一步的研究，在随机估计和控制问题，和2）必要的计算和困难，必须克服这些问题。一个例子的非线性，非高斯估计问题也得到解决。
In this paper, a general class of stochastic estimation and control problems is formulated from the Bayesian Decision-Theoretic viewpoint. A discussion as to how these problems can be solved step by step in principle and practice from this approach is presented. As a specific example, the closed form Wiener-Kalman solution for linear estimation in Gaussian noise is derived. The purpose of the paper is to show that the Bayesian approach provides; 1) a general unifying framework within which to pursue further researches in stochastic estimation and control problems, and 2) the necessary computations and difficulties that must be overcome for these problems. An example of a nonlinear, non-Gaussian estimation problem is also solved.