Topic
Probability density function
About: Probability density function is a research topic. Over the lifetime, 22321 publications have been published within this topic receiving 422885 citations. The topic is also known as: probability function & PDF.
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TL;DR: The fundamental solution for the Cauchy problem is interpreted as a probability density of a self-similar non-Markovian stochastic process related to a phenomenon of sub-diffusion (the variance grows in time sub-linearly).
166 citations
TL;DR: The formalism of the continuous-time random walk is applied to data on the U.S. dollar-deutsche mark future exchange, finding good agreement between theory and the observed data.
Abstract: We apply the formalism of the continuous-time random walk to the study of financial data. The entire distribution of prices can be obtained once two auxiliary densities are known. These are the probability densities for the pausing time between successive jumps and the corresponding probability density for the magnitude of a jump. We have applied the formalism to data on the U.S. dollar-deutsche mark future exchange, finding good agreement between theory and the observed data.
165 citations
TL;DR: In this article, asymptotic expansions for the exponential growth rate (Lyapunov exponent) and rotation number of the random oscillator were constructed when the noise is large, small, rapidly varying or slowly varying.
Abstract: We construct asymptotic expansions for the exponential growth rate (Lyapunov exponent) and rotation number of the random oscillator when the noise is large, small, rapidly varying or slowly varying. We then apply our results to problems in the stability of the random oscillator, the spectrum of the one-dimensional random Schr6dinger operator and wave propagation in a one-dimensional random medium.
165 citations
TL;DR: In this article, a fatigue reliability model which integrates the probability distribution of hot spot stress range with a continuous probabilistic formulation of Miner's damage cumulative rule is developed for fatigue life and reliability evaluation of steel bridges with long-term monitoring data.
Abstract: A fatigue reliability model which integrates the probability distribution of hot spot stress range with a continuous probabilistic formulation of Miner's damage cumulative rule is developed for fatigue life and reliability evaluation of steel bridges with long-term monitoring data. By considering both the nominal stress obtained by measurements and the corresponding stress concentration factor (SCF) as random variables, a probabilistic model of the hot spot stress is formulated with the use of the S-N curve and the Miner's rule, which is then used to evaluate the fatigue life and failure probability with the aid of structural reliability theory. The proposed method is illustrated using the long-term strain monitoring data from the instrumented Tsing Ma Bridge. A standard daily stress spectrum accounting for highway traffic, railway traffic, and typhoon effects is derived by use of the monitoring data. Then global and local finite element models (FEMs) of the bridge are developed for numerically calculating the SCFs at fatigue-susceptible locations, while the stochastic characteristics of SCF for a typical welded T-joint are obtained by full-scale model experiments of a railway beam section of the bridge. A multimodal probability density function (PDF) of the stress range is derived from the monitoring data using the finite mixed Weibull distributions in conjunction with a hybrid parameter estimation algorithm. The failure probability and reliability index versus fatigue life are achieved from the obtained joint PDF of the hot spot stress in terms of the nominal stress and SCF random variables.
165 citations
TL;DR: In this article, the authors study the consistency of the posterior from a Bernstein prior and show that the posterior is weakly consistent for any distribution function P o on [0, 1] with continuous and bounded Lebesgue density.
Abstract: A Bernstein prior is a probability measure on the space of all the distribution functions on [0, 1]. Under very general assumptions, it selects absolutely continuous distribution functions, whose densities are mixtures of known beta densities. The Bemstein prior is of interest in Bayesian nonparametric inference with continuous data. We study the consistency of the posterior from a Bernstein prior. We first show that, under mild assumptions, the posterior is weakly consistent for any distribution function P o on [0, 1] with continuous and bounded Lebesgue density. With slightly stronger assumptions on the prior, the posterior is also Hellinger consistent. This implies that the predictive density from a Bernstein prior, which is a Bayesian density estimate, converges in the Hellinger sense to the true density (assuming that it is continuous and bounded). We also study a sieve maximum likelihood version of the density estimator and show that it is also Hellinger consistent under weak assumptions. When the order of the Bernstein polynomial, i.e. the number of components in the beta distribution mixture, is truncated, we show that under mild restrictions the posterior concentrates on the set of pseudotrue densities. Finally, we study the behaviour of the predictive density numerically and we also study a hybrid Bayes-maximum likelihood density estimator.
164 citations