While the Marr/Albus cerebellar model expects Purkinje cells to behave linearly and have noise free outputs, physiological studies suggest otherwise. A cerebellar model based on ensembles of neurons encoding Probability Density Functions (PDF’s) described here may resolve these contradictions. Our simulations show that a PDF based model can describe the noisy output of these cells, and more imp...

Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are posterior probabilities of the models generating the forecasts and reflect the forecasts...

We propose DE-RNN to learn the probability density function (PDF) of a nonlinear time series, and compute the temporal evolution of the PDF for a probabilistic forecast. A Recurrent Neural Network (RNN) based model is employed to learn a nonlinear operator for temporal evolution of the stochastic process. We use a softmax layer for a numerical discretization of a PDF, which transforms a functio...

The probability density functions (PDF) of the firstand second-phase variation for multipath envelope in space are derived based on a wideband measured database at 5.3 GHz for indoor and outdoor mobile communications. The first-phase variation follows either a symmetrical or asymmetrical Gaussian distribution, and the second-phase variation follows a symmetrical Gaussian distribution with large...

In this paper an analytical model is presented in order to capture multimedia traffic characteristics. Multimedia source is modeled by a Switched Batch Bernoulli Process (SBBP) model and traffic characteristics are evaluated in terms of autoand cross correlation functions and IDI of the multimedia conference data flow. Finally, the model is applied to calculate the probability density function ...

A recent study of non-extensive phase transitions in nuclei and nuclear clusters needs a probability model compatible with the appropriate Hamiltonian. For magnetic molecules a representation of the evolution by a Markov process achieves the required probability model that is used to study the probability density function (PDF) of the order parameter, i.e., the magnetization. The existence of o...

Using a suite of self-similar cosmological simulations, we measure the probability distribution functions (PDFs) real-space density, redshift-space and their geometric mean. We find that density PDF is well-described by function two parameters: $n_s$, spectral slope, $\sigma_L$, linear rms fluctuation. For mean real- densities, introduce third parameter, $s_L={\sqrt{\langle(dv^L_{\rm pec}/dr)^2...