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.
Shaarawy, S. (1992). Bayesian Classification with Multivariate Autoregressive Processes. The Egyptian Statistical Journal, 36(2), 346-356. doi: 10.21608/esju.1992.314871
MLA
Samir M. Shaarawy. "Bayesian Classification with Multivariate Autoregressive Processes", The Egyptian Statistical Journal, 36, 2, 1992, 346-356. doi: 10.21608/esju.1992.314871
HARVARD
Shaarawy, S. (1992). 'Bayesian Classification with Multivariate Autoregressive Processes', The Egyptian Statistical Journal, 36(2), pp. 346-356. doi: 10.21608/esju.1992.314871
VANCOUVER
Shaarawy, S. Bayesian Classification with Multivariate Autoregressive Processes. The Egyptian Statistical Journal, 1992; 36(2): 346-356. doi: 10.21608/esju.1992.314871
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