Inference for dynamic and latent variable models via iterated, perturbed Bayes maps
Inference for dynamic and latent variable models via iterated, perturbed Bayes maps
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DOI：
10.1073/pnas.1410597112
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发表时间：
2015-01-20
影响因子：
11.1
通讯作者：
King, Aaron A.
中科院分区：
文献类型：
--
作者：
Ionides, Edward L.;Dao Nguyen;King, Aaron A.
迭代滤波算法是潜变量模型的随机优化过程，其递归地将参数扰动与潜变量重构结合联合收割机。以前，这些算法的理论支持一直是基于使用扰动参数的条件矩近似的对数似然函数的导数。在这里，介绍了一种理论方法的基础上收敛的迭代贝叶斯映射。由这一理论支持的算法显示了大量的数值改进的计算挑战推断参数的部分观察马尔可夫过程。
Iterated filtering algorithms are stochastic optimization procedures for latent variable models that recursively combine parameter perturbations with latent variable reconstruction. Previously, theoretical support for these algorithms has been based on the use of conditional moments of perturbed parameters to approximate derivatives of the log likelihood function. Here, a theoretical approach is introduced based on the convergence of an iterated Bayes map. An algorithm supported by this theory displays substantial numerical improvement on the computational challenge of inferring parameters of a partially observed Markov process.