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FILTERING AND SMOOTHING IN STATE SPACE MODELS WITH PARTIALLY DIFFUSE INITIAL CONDITIONS
Author(s) -
Ansley Craig F.,
Kohn Robert
Publication year - 1990
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1990.tb00058.x
Subject(s) - smoothing , kalman filter , mathematics , state vector , state space , state (computer science) , algorithm , annals , state space representation , filter (signal processing) , space (punctuation) , data assimilation , mathematical optimization , computer science , statistics , physics , classical mechanics , computer vision , operating system , ancient history , meteorology , history
. Ansley and Kohn ( Annals of Statistics , 1985) generalized the Kalman filter to handle state space models with partially diffuse initial conditions and used this filter to compute the marginal likelihood of the observations efficiently. In this paper we simplify the algorithm and make it numerically more accurate and operationally more efficient. Based on this filtering algorithm we obtain a corresponding smoothing algorithm for the state vector.