Can a relatively small numerical weather prediction ensemble produce any more forecast information than can be reproduced by a Gaussian probability density function (PDF)? This question is examined using site-specific probability forecasts from the UK Met Office. These forecasts are based on the 51-member Ensemble Prediction System of the European Centre for Medium-range Weather Forecasts. Veri...

Development of probabilistic sensitivities is frequently considered an essential component of a probabilistic analysis and often critical towards understanding the physical mechanisms underlying failure and modifying the design to mitigate and manage risk. One useful sensitivity is the partial derivative of the probability-of-failure and/or the system response with respect to the parameters of ...

In this paper the receiver for demodulation of M-FSK signals in the presence of Gaussian noise, intersymbol interference and Rice fading will be considered. Probability density function (PDF) of M-ary FSK signals in the presence of noise, interference and fading will be derived. Key-words: M-ary Frequency Shift Keying, Probability Density Function, Gaussian noise, Intersymbol Interference, Rice...

An original method to compute the extreme value distribution and dynamic reliability of stochastic structures is presented. A virtual stochastic process, related to the extreme value of the dynamic responses of stochastic structures, is constructed firstly, such that the extreme value is the sectioned random variable. A joint probability density equation is then deduced with the probability den...

We propose a new method for background modeling. Our method is based on the two complementary approaches. One uses the probability density function (PDF) to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. Then, foreground object is detected based on the estimated PDF. The method is based on the evaluation of the local texture at pixel-le...

We study the uncertainties of quantum mechanical observables, quantified by standard deviation (square root variance) in Haar-distributed random pure states. derive analytically probability density functions (PDFs) arbitrary qubit observables. Based on these PDFs, uncertainty regions observables are characterized supports PDFs. The state-independent relations then transformed into optimization ...