Abstract-This paper presents a new unifying continuous probability density function. It is shown that the continuous distributions Weibull-, gamma, Erlang-, x2 and exponential distributions can be derived from the proposed density function. 1.

value for one variable, the other one can be calculated as a means for prediction. In this paper an approach for the extraction of functional models from probability density functions is proposed. The transformation of the conditional probability density function into a single value or a set

This paper deals with composition of probability density functions (pdfs) in a sense of composition

Abstract. The state price density of a basket, even under uncorrelated Black–Scholes dynamics, does not allow for a closed from density. (This may be rephrased as statement on the sum of lognormals and is especially annoying for such are used most frequently in Financial and Actuarial Mathematics

Structural reliability depends on uncertainties in resistance and loads. In many practical cases the resistance dominates and a reduction of uncertainty about resistance isan effective way of increasing safety. It can be accomplished by proof loading. A truncated istribution is considered and reliability indices are calculated for various proof load levels. The structural reliability is sensitive to proof loading for larger coefficients of variation of resistance. A Bayesian approach is applied to develop aposterior distribution for resistance, after proof loading. Reliability indices are calculated for various ratios of the coefficients of variation of load and resistance.

ln A and E/R, which is in accordance with their 2D normal probability density function (pdf). The upper and the lower edges of the 1D normal distribution of ln k correspond to the two opposite edge regions of the 2D pdf of the transformed Arrhenius parameters. Changing the temperature, these edge

Assessment of a three-dimensional line-of-response probability density function system matrix

Mixtures of truncated exponentials (MTE) potentials are an alterna-tive to discretization for approx-imating probability density func-tions (PDF’s). This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid

Applications in computer vision involve statisti-cally analyzing an important class of constrained, non-negative functions, including probability density func-tions (in texture analysis), dynamic time-warping func-tions (in activity analysis), and re-parametrization or non-rigid registration

Abstract – In this paper, we address the problem of processing imprecisely known probability density functions by means of Bayesian estimation. The imprecise knowledge about probability density functions is given as stochastic uncertainty about their parameters. The proposed processing