Optimal Bayesian Classification When the Training Observations are Serially Dependent

2018

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DOI 10.1109/nysds.2018.8538943
Data confidence 85 high
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Abstract

In this study, we construct the optimal Bayesian classifier (OBC) when the training observations are serially dependent. To model the effect of dependency, we assume the training observations are generated from VAR(p), which is a multi-dimensional vector autoregressive process of order p.