A Monte Carlo method for an objective Bayesian procedure

This paper describes a method for an objective selection of the optimal prior distribution, or for adjusting its hyper-parameter, among the competing priors for a variety of Bayesian models. In order to implement this method, the integration of very high dimensional functions is required to get the normalizing constants of the posterior and even of the prior distribution. The logarithm of the high dimensional integral is reduced to the one-dimensional integration of a cerain function with respect to the scalar parameter over the range of the unit interval. Having decided the prior, the Bayes estimate or the posterior mean is used mainly here in addition to the posterior mode. All of these are based on the simulation of Gibbs distributions such as Metropolis' Monte Carlo algorithm. The improvement of the integration's accuracy is substantial in comparison with the conventional crude Monte Carlo integration. In the present method, we have essentially no practical restrictions in modeling the prior and the likelihood. Illustrative artificial data of the lattice system are given to show the practicability of the present procedure.     

Computer simulation of some dynamical properties of the Lorentz gas

We carried out molecular dynamics simulations of a Lorentz gas, consisting of a lone hydrogen molecule moving in a sea of stationary argon atoms. A Lennard-Jones form was assumed for the H₂-Ar potential. The calculations were performed at a reduced temperatureK ^* =kT/_{H 2–Ar} = 4.64 and at reduced densities ^*= _{Ar Ar} ³ in the range 0.074–0.414. The placement of Ar atoms was assumed to be random rather than dictated by equilibrium considerations. We followed the trajectories of many H₂ molecules, each of which is assigned in turn a velocity given by the Maxwell-Boltzmann distribution at the temperature of the simulation. Solving the equations of motion classically, we obtained the translational part of the incoherent dynamic structure factor for the H₂ molecule,S _tr(q, ). This was convoluted with the rotational structure factorS _rot(q, ) calculated assuming unhindered rotation to obtain the total structure factorS(q, ). Our results agree well with experimental data on this function obtained by Egelstaffet al. At the highest density ( ^*=0.414) we studied the dependence ofS(q, ) on system size (number of Ar atoms), number of H₂ molecules for which trajectories are generated, and the length of time over which these trajectories are followed.     

用透射积分法研究的穆斯堡尔谱学——一个在聚合二茂铁研究中的应用

本文利用透射积分法，在聚合二茂铁研究中发现，这种方法不但能够得到超精细参数，而且能够得到一般拟合方法得不到的另外两个重要参数—样品的无反冲分数和相应的德拜温度，由此能进一步了解到样品在结合中晶格的质量及化学键的强弱，为穆斯堡尔谱研究提供了一条新的途径。     

因子分析—伏安法同时测定波峰重叠的混合物组份

本文将目标因子分析用于伏安分析法同时测定波峰重叠的混合物组份。用此法对Ｐｂ（Ⅱ）、Ｔｌ（Ｉ）混合体系的导数脉冲伏安重叠峰数据进行解析，获得了较理想的结果。     

目标因子分光光度法同时测定中成药复方五味子中三项活性成分

将目标因子分光光度法应用于五味子提取液中五味子甲素、五味子乙素、五味子醇甲等三项活性成分的同时测定,介绍了基本原理和具体的分析步骤,运用计算机VC 语言对试验数据进行回归分析。试验结果表明,目标因子分光光度法对样品各组分的平均回收率在98.9%-106.7%之间,样品不经分离即可同时测定。     

对NO生理作用的新认识及其电化学实时检测

本文综述了近年来学术界对ＮＯ生理作用的新认识，并介绍了现场实时检测生物活体中释放的ＮＯ浓度的电化学方法．     

近红外漫反射光谱法对固态样品的无损分析

本文应用目标因子分析研究了固态样品的近红外漫反射光谱，用目标因子分析成功地确定了固态样品的物种数、物种种类和各物种的含量。     

制备参数对β-Mo2N0.78催化剂加氢脱硫活性影响的研究

以N2与H2的混合气为反应气，和三氧化钼进行多段程序升温反应，制得一种β晶型的氮化钼。以噻吩为模型化合物的常压加氢脱硫反应表明，β-Mo2N0.78具有较强的加氢脱硫活性和强的抗硫化性能。同时考察了预还原、反应温度以及氮化末温、升温速率、反应气中N2-H2比及氮化时间等制备参数对β-Mo2N0.78加氢脱硫活性的影响。研究发现，β-Mo2N0.78的加氢脱硫活性在320 ℃～400 ℃随反应温度的升高增强，而还原预处理会降低催化剂的活性。氮化末温、氮化时间、反应气组成和升温速率等制备参数对催化剂的活性有明显的影响：随着氮化末温的升高，所制备的催化剂催化加氢脱硫活性降低；在氮化末温恒温较长时间，可以引起制备催化剂的加氢脱硫活性下降；存在最佳的反应气组成和各段升温速率。小晶粒的β-Mo2N0.78具有强的加氢脱硫活性。     

完全未知混合体系中纯组份的红外光谱解析

提出一种新的目标转化因子分析，其初始向量的构造基于实际最大差异光谱，无需分离即可从红外混合体系中解析出纯组份光谱。该算法简单易懂，不必迭代，运算量小，应用于模拟及实际体系，结果令人满意。     

On the invariant measures for the two-dimensional euler flow

It is proven that the canonical Gibbs measure associated with a gas of vortices of intensity ± converges, in the limitN, 0,Nconst, to a Gaussian measure, which is invariant for the two-dimensional Euler equation.On leave from Dipartimento di Matematica Università di Roma Tor Vergata Roma, Italy.On leave from Dipartimento di Matematica Università di Roma La Sapienza, Roma, Italy.