The log likelihood function is as follows:

mixture by maximum likelihood: denormix package

Wilcox, Gebbie & Mbambiso Spin, stochastic factor models, and a genetic algorithm A non-parametric clustering technique

penalized likelihood functions. The proposed ideas are widely applicable. They are readily applied to a variety of parametric models such as generalized linear models and robust regression models. They can also be applied easily to nonparametric modeling by using wavelets and splines. Rates of convergence

The output of a classifier should be a calibrated posterior probability to enable post-processing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score

approximation to experimental band–power likelihood functions is proposed, which requires only limited experimental details. Comparison with the full likelihood function calculated for several experiments shows that the approximation works well. KEYWORDS: 1.

We generalize the maximum likelihood method to non-Gaussian distribution functions by means of the multivariate Edgeworth expansion. We stress the potential interest of this technique in all those cosmological problems in which the determination of a non-Gaussian signature is relevant, e

For a general stochastic signal in white noise absolute continuity is proved and the Radon-Nikodym derivative is given. These results were stated in a previous paper (Duncan 1968). Independent of the absolute continuity result, a modification is proved for the hypothesis with signal present. 1.

Probabilistic graphical models •Represent complex dependencies between

All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.