We introduce in this chapter the correlation and covariance matrices of a complex random vector. The Hermitian nature of these matrices allows their diagonalization in the basis of their orthogonal eigenvectors. These concepts are discussed on jointly Gaussian variables. We study the principal component...
Complex White Gaussian Noise Complex Work Unit Complex Zero Decreasing Sequences Complex-Chebyshev Functional Link Neural Network Complex-Choice Petri Net Complex-compound sentence Complex-Coordinate Coupled-Landau-Channel Complex-Envelope Finite-Difference Time Domain ...
operations and final carrying operations (to reduce the 56-bit coefficients back down to 25 bits) can be efficiently implemented using shuffles, which we havepreviously usedin the context of fast Gaussian random number generation, and any arithmetic operations are performed across all threads in ...
α is the intercept of the model; XE and XT are the environmental and technical variables (number of reads, average length of reads), respectively; βE and βT are the vectors of the environmental and technical coefficients, respectively; and ε is the randomly distributed Gaussian error N (...
Finally, a set of Gaussian random vectors will be generated within the Pareto front area if the number of cluster center vectors is not enough. 3) The proposed LC-MaOEA is examined on DTLZ5 and DTLZ6 problems with 5 to 40 objectives and compared with four state-of-the-art evolutionary...
the hidden vector in the model graph is a Gaussian distributed random variable \({Z}_{k}\), the fusion coding \({\varepsilon }_{tar}\) is connected with the context vector \({V}_{C}\), so that the decoder can generate different motion contours. so that the decoder can generate ...
The complexity of local structures is not well described by Random Markov Gaussian densities whereas Hidden Markov Models can be used to capture higher order statistics. The correlations between coefficients at same scale are modeled by Hidden Markov Chain Model whereas the correlation between ...
Let us define the rth order cumulants of a random vector X in CM,κa1⋯ar=κ(Xa1,…,Xar), for 1≤ai≤M exactly as for a random vector in RM. That is, for t in CM, lnEexp(tTX)=∑r=1∞κa1⋯arta1⋯tar/r!, where we use the tensor summation convention of implicit summa...
RegisterLog in Sign up with one click: Facebook Twitter Google Share on Facebook endometrial hyperplasia (redirected fromComplex hyperplasia) endometrial hyperplasia increase in the number of endometrial glands, usually secondary to hyperestrinism; classified as simple hyperplasia, complex hyperplasia, or...
such that ∑k=1Kπk=1 and πk>0 for each k, and we assume that, conditionally on Z, the random vector ββ follows a multivariate Gaussian distribution, that is for each cluster ββ|(Zk=1)=ββk∼N(μμk,\unicodex03A3\unicodex03A3k) where μμk=(μ1,k,…,μp,k)T and ...