Flow systems exploiting in-line prior assays

An expert sequential injection system involving a prior assay is proposed for spectrophotometric determination of phosphate and eventually zinc in soil extracts. The result of phosphate determination is the basis for a concentration-oriented decision regarding to the need or not for zinc determination. Zinc was only determined if a threshold value (peak height corresponding to 5.0 mg l⁻¹ P) was surpassed. The methods involved formation of molybdenum blue and the Rhodamine 6G/ammonium thiocyanate/Zn²⁺ ternary complex. Variations in the threshold value were < 2% during 4 h operating periods, false responses were not verified, and the analytical time was reduced in about 30%. Precise results (R.S.D. <3% P and < 1% Zn) in agreement with spectrophotometry and flame atomic absorption spectrometry were obtained. The innovation permits faster information processing, as well as a reduction in the number of measurements, number of analytical steps, laboratorial time, and consumption of sample and reagents, thus waste generation.     

正态分布在不同先验下的Bayes估计及风险比较

本文讨论在均值未知,方差已知的正态分布情况下通过在共轭先验以及Jeffreys先验二种先验下的Bayes估计问题,在平方损失函数下和线性损失函数下Bayes风险的比较.数据计算可以看出,在Jeffreys先验下的Bayes风险要比在共轭先验下的Bayes风险要大,但是当样本量增大时,两者的后验风险越来越靠近.     

位置参数分布族中Fiducial分布与后验分布的关系

本文考虑本质位置参数分布族中,参数的Fiducial分布与后验分布的等同问题．首先讨论了如何给出Fiducial分布,分析结果表明以分布函数形式给出Fiducial分布要比密度函数形式合理,同时,证明了所给的Fiducial分布具有频率性质．然后,研究在参数受到单侧限制时,Fiducial分布与后验分布等同的问题,给出的充要条件是分布族为指数分布族,此时,先验分布是一个广义先验分布,它不能被Lebesgue测度控制．最后,证明了在参数限制在一个有限区间内时,Fiducial分布与任何先验(包括广义先验分布)下的后验分布不等同．     

On convergence of a semi-analytical method for American option pricing

We examine the valuation of American put options by a semi-analytical method, and obtain the prior estimate and the convergence of the approximate solution. Our proofs are based on the embedding theorem in Sobolev space and the theory of functional analysis, in particular, the theory of weak compactness. The results in this paper theoretically confirm empirical observations that these methods are accurate and computationally efficient.     

Asymptotically optimal empirical bayes procedures for selecting good

Let ∏₁,…,∏_k denote k independent populations, where a random observation from population ∏ _i has a uniform distribution over the interval (0,θ _i ) and θ _i is a realization of a random variable having an unknown prior distribution G _i . Population ∏ _i is said to be a good population if θ _i ≥θ₀, where θ₀ is a given, positive number. This paper provides a sequence of empirical Bayes procedures for selecting the good populationsamong ∏₁,…,∏ _k . It is shown that these procedures are asymptotically optimal and that the order of associated convergence rates is O(n^-r/4) for some r, 0<r<2, where n is the number of accumulated past observations

at hand     

Performance gain of displacement receiver with optimized prior probability

We numerically study the performance of the displacement based quantum receiver for the discrimination of weak 3- and 4-phase-shift keyed (PSK) coherent state signals. We show that due to the nontrivial asymmetry of the receiver structure, optimization of the prior probability increases the mutual information and achieves sub-shot-noise limit discrimination. Moreover, we estimate the cutoff rate for a 4-PSK signal and confirm that the prior probability optimization shortens the code length for a given decoding error criterion. Such consideration for the asymmetric channel matrix is essential in a study of the compassable quantum receiver.     

A Bayesian approach to assess data from radionuclide activity analyses in environmental samples

A Bayesian statistical approach is introduced to assess experimental data from the analyses of radionuclide activity concentration in environmental samples (low activities). A theoretical model has been developed that allows the use of known prior information about the value of the measurand (activity), together with the experimental value determined through the measurement. The model has been applied to data of the Inter-laboratory Proficiency Test organised periodically among Spanish environmental radioactivity laboratories that are producing the radiochemical results for the Spanish radioactive monitoring network. A global improvement of laboratories performance is produced when this prior information is taken into account. The prior information used in this methodology is an interval within which the activity is known to be contained, but it could be extended to any other experimental quantity with a different type of prior information available.     

基于D-S证据理论的Bayes信息融合

在Bayes统计中,验前分布的获取和表示是一个关键问题,尤其在验前信息多源型的情况下,更需要合理客观地综合利用这些信息.针对实际现实中信息的多源性和不确定性,本文给出了一种基于D-S证据理论的多源总体验前分布的融合方法,并通过实例说明了该方法在Bayes先验信息融合中的应用.     

A nonlinear multi-classification knowledge-based kernel machine

This paper presents a knowledge-based nonlinear kernel classification model for multi-category discrimination of sets or objects with prior knowledge. A kernel function is employed to find a nonlinear classifier capable of discriminating future points into an appropriate class. The prior knowledge is in the form of multiple polyhedral sets belonging to one or more categories or classes, and it is introduced as additional constraints into the formulation of the regularized nonlinear kernel least squares multi-class support vector machine model. The resulting formulation leads to a linear system of equations that can be solved using matrix methods or iterative methods. This work extends previous work (Oladunni et al. in ICCS 2006, Lecture notes in Computer Science, Part I, LNCS, vol 3991. Springer, Berlin, pp 188–195, 2006) that incorporated similar prior knowledge into a regularized linear least squares multi-class model. To evaluate the model, data and prior knowledge from the two-phase flow regimes in pipes were used to train and test the proposed formulation.     

非线性Kundu方程的弱守恒差分格式

本文考察了一类非线性Kundu方程的周期初值问题,提出了一种弱守恒的差分格式,对其差分解作了先验估计,证明了格式的收敛性与稳定性,最后,通过数值计算检验了格式的可信性.