This paper introduces MEASURES of INFORMATION for Bayesian analysis when the support of data distribution is truncated progressively. The focus is on the lifetime distributions where the support is truncated at the current age t ³ 0. Notions of uncertainty and INFORMATION are presented and operationalized by Shannon entropy, Kullback-Leibler INFORMATION, and mutual INFORMATION. Dynamic updatings of prior distribution of the parameter of lifetime distribution based on observing a survival at age t and observing a failure or the residual lifetime beyond t are presented. Dynamic MEASURES of INFORMATION provided by the data about the parameter of lifetime distribution, and dynamic predictive INFORMATION are introduced. These MEASURES are applied to two well-known lifetime models. The paper concludes with some remarks on use of generalized uncertainty and INFORMATION MEASURES, and some topics for further research.

In applications of differential geometry to problems of parametric inference, the notion of divergence is often used to measure the separation between two parametric densities. Among them, in this paper, we will verify MEASURES such as Kullback-Leibler INFORMATION, J-divergence, Hellinger distance, a -Divergence, ... and so on. Properties and results related to distance between probability distributions derived via copula functions. Some inequalities are obtained in view of the dependence and INFORMATION MEASURES.

In this paper, characterization results via optimality criteria of entropy and maximum entropy for the Pareto family are discussed. Order statistics results related to this family has an important role in statistical inference. We devoted characterization problems in view of the order statistics and entropy linked with the Pareto families in univariate cases. Also, for the above family, results via MEASURES such as kullback Leibler INFORMATION are derived.

The revolution in the field of Fawa shows the necessity of continuous development and evaluation of INFORMATION and communication systems. The purpose of this research is to provide evaluation models in INFORMATION systems development methods. The sample of this research was 40 specialists in the development and implementation of INFORMATION systems, who were selected by the purposeful sampling method. The research tool was made by the researcher’s questionnaire for which Cronbach’s alpha test showed acceptable validity and reliability. The collected data were analyzed along with statistical tests during experiments. The findings have shown that the necessary operations in the traditional method are: needs assessment, feasibility study, conceptual and logical design, deployment and implementation, maintenance, reporting and documentation, setting goals, determining performance indicators, cost-benefit analysis and detailed design. is the system. Prototyping methods include: system improvement and development, needs assessment, conceptual and logical design, programming, testing and evaluation, implementation and physical design. In the life cycle method, they are: design, requirements assessment, feasibility study, feasibility report, identification of user INFORMATION requirements, conceptual and logical design, programming, evaluation and testing, deployment and implementation, delivery, maintenance and development of the system, report and It is documentation. In the organization-oriented method, they are: determining systems and subsystems, INFORMATION requirements, managing resources and formulating an INFORMATION strategy. In the object-oriented method, they are: examining the system’s INFORMATION interaction, system modeling, identifying important events, change diagrams, modeling processes, and drawing data flow diagrams. As a result: different organizations, situations and INFORMATION systems require the use of different implementation methods, and designers and implementers must act towards the realization of the standard operational MEASURES of each method.

Maximum of the Renyi entropy and the Tsallis entropy are generalization of the maximum entropy for a larger class of Shannon entropy. In this paper we introduce the maximum Renyi entropy and some of the attributes of distributions which have maximum Renyi entropy investigated. The form of distributions with maximum Renyi entropy is power so we state some properties of these distributions and we have a new form of the Renyi entropy. After pointing the topics of minimum Renyi divergence, some other points in this relation have been discussed. An another form of Renyi divergence have also obtained. Therefore we discussed some of the economic applications of the maximum entropy. Meanwhile, the review of the Csiszar INFORMATION measure, the general form of distributions with minimum Renyi divergence have obtained.