Bayesian Classification with Multivariate Autoregressive Processes | ||||
The Egyptian Statistical Journal | ||||
Article 12, Volume 36, Issue 2, December 1992, Page 346-356 | ||||
Document Type: Original Article | ||||
DOI: 10.21608/esju.1992.314871 | ||||
View on SCiNiTO | ||||
Author | ||||
Samir M. Shaarawy | ||||
Abstract | ||||
The main objective of this paper is to develop a Bayesian technique that can be used to assign a multivariate time series realization to one of several. multivariate autoregressive sources, with unknown coefficients, that share a common known order and unknown precision matrix. The foundation of the proposed assignment technique is to derive the marginal posterior mass function of a classification vector using the exact conditional likelihood function. A multivariate time series realization is assigned to that multivariate autoregressive process with the largest posterior probability. | ||||
Keywords | ||||
Bayesian Analysis; Classification; Multivariate Autoregressive Processes | ||||
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