In this we paper study the problem of combining the outputs of the members of an ensemble of neural networks. We review the commonly used methods and thoroughly derive a cost function from a maximum likelihood perspective, that can be minimized in order to obtain maximum likelihood weights. The solution is shown to be closely related to a well known statistical method. The various combination m...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

A Bayesian methodology is used to assess the information content of categorical, probabilistic forecasts of specific variables derived from a general circulation model (GCM) forecast ensemble, and to combine a ‘‘prior’’ forecast (climatological probabilities of each category) with a categorical probabilistic forecast derived from a GCM ensemble to develop posterior, or ‘‘regularized’’ categoric...

Although the random sum distribution has been well-studied in probability theory, inference for the mean of such distribution is very limited in the literature. In this paper, two approaches are proposed to obtain inference for the mean of the Poisson-Exponential distribution. Both proposed approaches require the log-likelihood function of the Poisson-Exponential distribution, but the exact for...

BACKGROUND AND OBJECTIVE Acute retinal necrosis (ARN) may lead to severe visual loss because of its rapid progression and high likelihood of retinal detachment (RD). This study investigates whether combination systemic and intravitreal antiviral therapy is superior to systemic antiviral therapy alone. PATIENTS AND METHODS Single-center, interventional, comparative case series of patients with...

The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive little or no education in the conceptual underpinnings of the approach. Here we provide a primer on maximum likelihood and some important extensions which have proven useful in epidemiologic research, and which reveal connections between maximum likelihood and Bayesian methods. For a given data set...

Abstract Likelihood analysis is typically limited to normally distributed noise due the difficulty of determining probability density function complex, high-dimensional, non-Gaussian, and anisotropic noise. This a major limitation for precision measurements in many domains science, including astrophysics, example, cosmic microwave background, gravitational waves, lensing, exoplanets. work prese...