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A unifying probability density function
 Applied Mathematics Letters
, 1993
"... AbstractThis 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. ..."
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Cited by 1 (1 self)
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AbstractThis 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.
Functional Models and Probability Density Functions
"... There exist many approaches to discern a functional relationship between two variables. A functional model is useful for two reasons: Firstly, if the function is a relatively simple model in the plane, it provides us with qualitative information about the relationship. Secondly, given a fixed va ..."
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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
Composition of Probability Density Functions  Optimizing Approach
 of International Series on Advanced Intelligence
, 2004
"... This paper deals with composition of probability density functions (pdfs) in a sense of composition ..."
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Cited by 11 (2 self)
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This paper deals with composition of probability density functions (pdfs) in a sense of composition
On the probability density function of baskets
, 2013
"... 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.) ..."
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Cited by 2 (2 self)
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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
Probability density function
, 1983
"... 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 reliab ..."
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Cited by 1 (0 self)
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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.
Uncertainty parameter · Probability density function
"... Abstract The temperature dependence of rate coefficient k is usually described by the Arrhenius expression ln k = ln A − (E/R)T −1. Chemical kinetics databases contain the recommended values of Arrhenius parameters A and E, the uncertainty parameter f (T) of the rate coefficient and temperature rang ..."
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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
probability density function system matrix for PET
, 2012
"... Assessment of a threedimensional lineofresponse probability density function system matrix ..."
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Assessment of a threedimensional lineofresponse probability density function system matrix
Approximating probability density functions with mixtures of truncated exponentials
 Proceedings of the Tenth Conference on Information Processing and Management of Uncertainty in KnowledgeBased Systems (IPMU04), 2004
"... Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for approximating probability density functions (PDF’s). This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesi ..."
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Cited by 32 (21 self)
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Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for approximating probability density functions (PDF’s). This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid
Riemannian analysis of probability density functions with applications in vision
 In 2007 IEEE CVPR’07
, 2007
"... Applications in computer vision involve statistically analyzing an important class of constrained, nonnegative functions, including probability density functions (in texture analysis), dynamic timewarping functions (in activity analysis), and reparametrization or nonrigid registration functio ..."
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Cited by 50 (5 self)
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Applications in computer vision involve statistically analyzing an important class of constrained, nonnegative functions, including probability density functions (in texture analysis), dynamic timewarping functions (in activity analysis), and reparametrization or nonrigid registration
Bayesian Estimation with Uncertain Parameters of Probability Density Functions
"... 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 of this speci ..."
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Cited by 1 (1 self)
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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
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