In this paper we discuss a step further some convergence and continuity problems of distribution function on R^i. We give the following results: (1)distribution function F(x_1,…,x_k) on R^k is continuous if and only ...In this paper we discuss a step further some convergence and continuity problems of distribution function on R^i. We give the following results: (1)distribution function F(x_1,…,x_k) on R^k is continuous if and only if all marginal distribution functions of F is continuous on R^1. (2)If limF_n(x_1,……,x_k)=F(x_1,…,x_k) and limF_n(x_1—0,…,x_k—0)=F(x_1—0,…,x_k—0) at all non-continuity points of F, then展开更多
The more diverse the ways and means of information acquisition are,the more complex and various the types of information are. The qualities of available information are usually uncertain,vague,imprecise,incomplete,and...The more diverse the ways and means of information acquisition are,the more complex and various the types of information are. The qualities of available information are usually uncertain,vague,imprecise,incomplete,and so on. However,the information is modeled and fused traditionally in particular,name some of the known theories: evidential,fuzzy sets,possibilistic,rough sets or conditional events,etc. For several years,researchers have explored the unification of theories enabling the fusion of multisource information and have finally considered random set theory as a powerful mathematical tool. This paper attempts to overall review the close relationships between random set theory and other theories,and introduce recent research results which present how different types of information can be dealt with in this unified framework. Finally,some possible future directions are discussed.展开更多
Benford＇s law is logarithmic law for distribution of leading digits formulated by P[D=d]= log（1＋1/d） where d is leading digit or group of digits. It＇s named by Frank Albert Benford （1938） who formulated mathema...Benford＇s law is logarithmic law for distribution of leading digits formulated by P[D=d]= log（1＋1/d） where d is leading digit or group of digits. It＇s named by Frank Albert Benford （1938） who formulated mathematical model of this probability. Befbre him, the same observation was made by Simon Newcomb. This law has changed usual preasumption of equal probability of each digit on each position in number.The main characteristic properties of this law are base, scale, sum, inverse and product invariance. Base invariance means that logarithmic law is valid for any base. Inverse invariance means that logarithmic law for leading digits holds for inverse values in sample. Multiplication invariance means that if random variable X follows Benford＇s law and Y is arbitrary random variable with continuous density then XY follows Benford＇s law too. Sum invariance means that sums of significand are the same for any leading digit or group of digits. In this text method of testing sum invariance property is proposed.展开更多
In this work, the MMC-TDGL equation, a stochastic Cahn-Hilliard equation, is solved numerically by using the finite difference method in combination with a convex splitting technique of the energy functional.For the n...In this work, the MMC-TDGL equation, a stochastic Cahn-Hilliard equation, is solved numerically by using the finite difference method in combination with a convex splitting technique of the energy functional.For the non-stochastic case, we develop an unconditionally energy stable difference scheme which is proved to be uniquely solvable. For the stochastic case, by adopting the same splitting of the energy functional, we construct a similar and uniquely solvable difference scheme with the discretized stochastic term. The resulted schemes are nonlinear and solved by Newton iteration. For the long time simulation, an adaptive time stepping strategy is developed based on both first- and second-order derivatives of the energy. Numerical experiments are carried out to verify the energy stability, the efficiency of the adaptive time stepping and the effect of the stochastic term.展开更多
文摘In this paper we discuss a step further some convergence and continuity problems of distribution function on R^i. We give the following results: (1)distribution function F(x_1,…,x_k) on R^k is continuous if and only if all marginal distribution functions of F is continuous on R^1. (2)If limF_n(x_1,……,x_k)=F(x_1,…,x_k) and limF_n(x_1—0,…,x_k—0)=F(x_1—0,…,x_k—0) at all non-continuity points of F, then
基金Supported in part by the NSFC (No.60934009,60874105)the ZJNSF (Y1080422, R106745)NCET (08-0345)
文摘The more diverse the ways and means of information acquisition are,the more complex and various the types of information are. The qualities of available information are usually uncertain,vague,imprecise,incomplete,and so on. However,the information is modeled and fused traditionally in particular,name some of the known theories: evidential,fuzzy sets,possibilistic,rough sets or conditional events,etc. For several years,researchers have explored the unification of theories enabling the fusion of multisource information and have finally considered random set theory as a powerful mathematical tool. This paper attempts to overall review the close relationships between random set theory and other theories,and introduce recent research results which present how different types of information can be dealt with in this unified framework. Finally,some possible future directions are discussed.
文摘Benford＇s law is logarithmic law for distribution of leading digits formulated by P[D=d]= log（1＋1/d） where d is leading digit or group of digits. It＇s named by Frank Albert Benford （1938） who formulated mathematical model of this probability. Befbre him, the same observation was made by Simon Newcomb. This law has changed usual preasumption of equal probability of each digit on each position in number.The main characteristic properties of this law are base, scale, sum, inverse and product invariance. Base invariance means that logarithmic law is valid for any base. Inverse invariance means that logarithmic law for leading digits holds for inverse values in sample. Multiplication invariance means that if random variable X follows Benford＇s law and Y is arbitrary random variable with continuous density then XY follows Benford＇s law too. Sum invariance means that sums of significand are the same for any leading digit or group of digits. In this text method of testing sum invariance property is proposed.
基金supported by the Hong Kong General Research Fund (Grant Nos. 202112, 15302214 and 509213)National Natural Science Foundation of China/Research Grants Council Joint Research Scheme (Grant Nos. N HKBU204/12 and 11261160486)+1 种基金National Natural Science Foundation of China (Grant No. 11471046)the Ministry of Education Program for New Century Excellent Talents Project (Grant No. NCET-12-0053)
文摘In this work, the MMC-TDGL equation, a stochastic Cahn-Hilliard equation, is solved numerically by using the finite difference method in combination with a convex splitting technique of the energy functional.For the non-stochastic case, we develop an unconditionally energy stable difference scheme which is proved to be uniquely solvable. For the stochastic case, by adopting the same splitting of the energy functional, we construct a similar and uniquely solvable difference scheme with the discretized stochastic term. The resulted schemes are nonlinear and solved by Newton iteration. For the long time simulation, an adaptive time stepping strategy is developed based on both first- and second-order derivatives of the energy. Numerical experiments are carried out to verify the energy stability, the efficiency of the adaptive time stepping and the effect of the stochastic term.
