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Practical filtering with sequential parameter learning
Author(s) -
Polson Nicholas G.,
Stroud Jonathan R.,
Müller Peter
Publication year - 2008
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2007.00642.x
Subject(s) - smoothing , computer science , inference , curse of dimensionality , outlier , algorithm , benchmark (surveying) , particle filter , kernel smoother , artificial intelligence , machine learning , kalman filter , kernel method , geodesy , radial basis function kernel , support vector machine , computer vision , geography
Summary. The paper develops a simulation‐based approach to sequential parameter learning and filtering in general state space models. Our approach is based on approximating the target posterior by a mixture of fixed lag smoothing distributions. Parameter inference exploits a sufficient statistic structure and the methodology can be easily implemented by modifying state space smoothing algorithms. We avoid reweighting particles and hence sample degeneracy problems that plague particle filters that use sequential importance sampling. The method is illustrated by using two examples: a benchmark auto‐regressive model with observation error and a high dimensional dynamic spatiotemporal model. We show that the method provides accurate inference in the presence of outliers, model misspecification and high dimensionality.