In this paper, the multilinear normal distribution is introduced as an extension of the matrix-variate normal distribution. Basic properties such as marginal and conditional distributions, moments, and the characteristic function, are also presented. The estimation of parameters using a flip-flop algorithm is also briefly discussed.
Some recent papers consider estimation of a covariance matrix with Kronecker structure of higher order. Singull et al. (2012) and Manceur and Dutilleul (2013) extend the estimation procedure for the matrix normal distribution to the multilinear normal distribution of order three and Ohlson et al. (2013) consider the case of higher order tensors of order k.… (More)
In this paper, we discuss the nature specific continued fractions. We begin by examining and verifying the fact that the Golden Ratio, Φ, can be expressed as a continued fraction, and then move on to reiterating the same process for other irrational numbers such as √ 2. For the given process of verification, we use both analytical and computational… (More)