This study proposes an algebraic iterative method that approximates the error distribution model using a Gaussian mixture distribution, with the application of maximum likelihood estimation as a possible solution to the problem. The global maximisation of the likelihood function is carried out through ...
Closed form representations of the gradients and an approximation to the Hessian are given for an asymptotic approximation to the log likelihood function of a multidimensional autoregressive moving average Gaussian process. Their use for the numerical maximization of the likelih...
Lecture Notes in Computer Science, 2658 (2003), 427-435 In this paper we consider a new way to calculate the logarithm of the Likelihood Ratio Function for Gaussian signals. This approach is based on the standard Kalman filter. Its efficiency is substantiated theoretically, and numerical examples...
Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly...
We overcome the nondifferentiability of the Laplacian likelihood function by rewriting the ML problem as an exact weighted version of the Gaussian case, and compare two solution strategies. One of them is iterative, based on block coordinate descent, and uses SLNN as a subprocessing block. The ...
The combination method based on the product of the likelihoods associated with each set of observations reduced the uncertainties in posterior distributions of parameter estimates most significantly. It was also found that the likelihood function based on Gaussian probability density function was the best...
Thejoint probability density functionof the -th observation is where: is theprobabilityof the -th component of the mixture; the vector is the mean vector of the -th component; the matrix is thecovariance matrixof the -th component.
The combination method based on the product of the likelihoods associated with each set of observations reduced the uncertainties in posterior distributions of parameter estimates most significantly. It was also found that the likelihood function based on Gaussian probability density function was the best...
The sparsity and density of point cloud in the space cause the different weights in Gaussian distribution. What's more, as a probability density function (PDF), it requires the summation of weights is 1. In summary, GMM, as a statistical model, shows the probability distribution of the ...
The latter, originally derived in the Gaussian case, is based on the fact that the probability density function (p.d.f.) of the likelihood ratio (LR... YI Abramovich,O Besson - IEEE International Conference on Acoustics 被引量: 68发表: 2013年 Fast method for spreading sequence estimation ...