A comparison of approximate Bayesian forecasting methods for non-Gaussian time series
UNSPECIFIED. (2000) A comparison of approximate Bayesian forecasting methods for non-Gaussian time series. JOURNAL OF FORECASTING, 19 (2). pp. 135-148. ISSN 0277-6693Full text not available from this repository.
We present the results on the comparison of efficiency of approximate Bayesian methods for the analysis and forecasting of non-Gaussian dynamic processes. A numerical algorithm based on MCMC methods has been developed to carry out the Bayesian analysis of non-linear time series. Although the MCMC-based approach is not fast, it allows us to study the efficiency, in predicting future observations, of approximate propagation procedures that, being algebraic, have the practical advantage of being very quick. Copyright (C) 2000 John Wiley & Sons, Ltd.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
H Social Sciences > HD Industries. Land use. Labor
|Journal or Publication Title:||JOURNAL OF FORECASTING|
|Publisher:||JOHN WILEY & SONS LTD|
|Official Date:||March 2000|
|Number of Pages:||14|
|Page Range:||pp. 135-148|
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