Particle Filtering Applied to Robust Multivariate Likelihood Optimization in the Absence of a Closed-Form Solution

P. Closas, J. Fernández-Rubio, C. F. Prades
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引用次数: 1

Abstract

Sequential Monte Carlo (SMC) methods are studied to deal with multivariate optimization problems arising from Maximum Likelihood (ML) estimation approaches. We compare results to those obtained by other methods, showing faster convergence and improved robustness when local optimums are present in the cost function to optimize. This paper presents a SMC method to obtain ML estimates in general multivariate state-spaces where a closed-form solution cannot be obtained, reporting computer simulation results for a particular application.
粒子滤波在无封闭解的鲁棒多元似然优化中的应用
研究了序列蒙特卡罗(SMC)方法来处理极大似然(ML)估计方法引起的多变量优化问题。我们将结果与其他方法得到的结果进行了比较,结果表明,当需要优化的代价函数中存在局部最优时,收敛速度更快,鲁棒性更好。本文提出了一种SMC方法来获得一般多元状态空间中无法获得封闭解的ML估计,并报告了特定应用的计算机模拟结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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