Quite often, some additional information is available from different sources other than the parent population. In such cases, the density estimation problem becomes substantial. Suppose a random matrix loading information is independent of the random matrix we want to estimate its density. This paper proposes estimating the future density of the random–variate matrix from a normal distribution using the Bayesian scheme by contemplating the other source of available information. The Kullback–Leibler loss function is used to study the dominance property of the proposed estimator in support of our findings, both analytically and numerically.

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