Hilbert Space of Probability Density Functions Based on Aitchison Geometry

The set of probability functions is a convex subset of L1 and it does not have a linear space structure when using ordinary sum and multiplication by real constants. Moreover, difficulties arise when dealing with distances between densities. The crucial point is that usual distances are not invariant under relevant transformations of densities. To overcome these limitations, Aitchison＇s ideas on compositional data analysis are used, generalizing perturbation and power transformation, as well as the Aitchison inner product, to operations on probability density functions with support on a finite interval. With these operations at hand, it is shown that the set of bounded probability density functions on finite intervals is a pre-Hilbert space. A Hilbert space of densities, whose logarithm is square-integrable, is obtained as the natural completion of the pre-Hilbert space.     

Probability Density Functions of Task-processing Times for Deterministic,Time-varying Processor Efficiency

We consider a task being performed in an environment in which the work-rate (or efficiency of the processor varies with time in a deterministic manner. Given the probability density function (p.d.f.) of the total work-content of the task (i.e. processing time at 100% efficiency), we develop a closed-form expression for the p.d.f. of the task-processing time. Numerical examples are given.     

一类由一维概率密度函数构造多维概率密度函数的方法

将文献[1]给出的由一维连续型随机变量的概率密度函数构造二维连续型随机变量的概率密度函数的方法,推广为由一维连续型随机变量的概率密度函数构造三维连续型随机变量的概率密度函数的情况,并作出了证明和举例说明.说明利用本文的方法构造多维概率密度函数,其方法简单易行.     

Identification of Stochastically Perturbed Autonomous Systems from Temporal Sequences of Probability Density Functions

The paper introduces a method for reconstructing one-dimensional iterated maps that are driven by an external control input and subjected to an additive stochastic perturbation, from sequences of probability density functions that are generated by the stochastic dynamical systems and observed experimentally.     

NA样本下基于递推型核估计的概率密度函数的经验似然推断(英文)

在NA样本下, 本文研究了基于递推型估计的概率密度函数的置信区间的构造, 证明了分块经验似然比统计量的极限分布为χ~2分布, 并利用此结果构造了概率密度函数的经验似然置信区间.     

调整保险费率模型下的破产概率

本文主要讨论当保险费率按公司的盈余进行适当的调整时,如何求破产概率的问题．     

谐和与随机噪声联合作用下Duffing振子的双峰稳态密度

研究Duffing振子在谐和与随机噪声联合作用下系统响应的双峰稳态概率密度问题．用多尺度法分离了系统的快变项,得到了系统慢变项满足的随机微分方程．用线性化方法求出了双峰稳态概率密度的表达式．数值模拟表明提出的方法是有效的．     

基于偏差密度模型的小波估计

讨论了带加权分布且基于分层相协随机变量的密度函数估计问题,提出了线性小波估计器,并给出该估计器的L~p(1≤p∞)风险上界.     

概率密度函数的广义最小二乘估计

本文提出了应用线性回归的方法改良核密度估计的一种方法,改良的估计量为GLS估计.并讨论了几个与之相关的问题.     

概率密度演化理论的若干研究进展

介绍了随机动力系统中概率密度演化理论的基本方程与求解方法〖CX4〗．〖CX〗在此基础上,论述了广义概率密度演化方程求解的若干新进展,包括群演化方程及其求解、概率空间剖分的理性准则、点集加密技术与信息拓展方法等.     

示性函数在初等概率论中的应用

示性函数在实分析等课程中很基本且应用广泛,但在初等概率论教材里应用不多.本文举例说明示性函数可以帮助学生理解初等概率论中一些基本概念、结论并精简其中一些计算.     

Correction: An Application of Free Probability to Arithmetic Functions

In this paper, we study free probability on tensor product algebra \(\mathfrak {M} = M\,\otimes _{\mathbb {C}}\,{\mathcal {A}}\) of a \(W^{*}\)-algebra M and the algebra \({\mathcal {A}}\), consisting of all arithmetic functions equipped with the functional addition and the convolution. We study free-distributional data of certain elements of \(\mathfrak {M}\), and study freeness on \(\mathfrak {M}\), affected by fixed primes.     

A Perspective of the Functions and Criticisms of Trip Distribution Models

The purpose of this paper is to clarify from methodological and philosophical standpoints the status of trip distribution models and criticisms of them. The discussion first establishes the foundation of models in analogy. Then the analogy is identified in each of the more common trip distribution models. Criticisms of these models are viewed as observations on the appropriateness of the analogy and its use in each model. It is maintained that the relevance and validity of some of these criticisms depends on the philosophical light in which the function of models is viewed.     

A Discussion of Probability Functions and Constraints from a Variational Perspective

Set-Valued and Variational Analysis - Probability constraints are a popular modelling mechanism in applications. They help to model feasible decisions when the latter are taken prior to observing...     

Estimating the Density of the Residuals in Autoregressive Models

We consider a (nonlinear) autoregressive model with unknown parameters (vector θ). The aim is to estimate the density of the residuals by a kernel estimator. Since the residuals are not observed, the usual procedure for estimating the density of the residuals is the following: first, compute an estimator for θ; second, calculate the residuals by use of the estimated model; and third, calculate the kernel density estimator by use of these residuals. We show that the resulting density estimator is strong consistent at the best possible convergence rate. Moreover, we prove asymptotic normality of the estimator. This revised version was published online in June 2006 with corrections to the Cover Date.     

半参数两样本密度比率模型估计问题的一点注记

In this paper,a semiparametric two-sample density ratio model is considered and the empirical likelihood method is applied to obtain the parameters estimation.A commonly occurring problem in computing is that the empirical likelihood function may be a concaveconvex function.Here a simple Lagrange saddle point algorithm is presented for computing the saddle point of the empirical likelihood function when the Lagrange multiplier has no explicit solution.So we can obtain the maximum empirical likelihood estimation (MELE) of parameters.Monte Carlo simulations are presented to illustrate the Lagrange saddle point algorithm.