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491.
Yonghua Ji Subodha Kumar Vijay S. Mookerjee Suresh P. Sethi Denny Yeh 《Production and Operations Management》2011,20(6):889-904
We develop an optimal control model to maximize the net value provided by a software system over its useful life. The model determines the initial number of features in the system, the level of dynamic enhancement effort, and the lifetime of the system. The various factors affecting these optimal choices are systems characteristics (e.g., complexity, age, quality), user learning, and process maturity. We also consider that there is a time lag between the addition of a feature and the realization of its benefit to users. The basic model is extended to consider the decision of replacing the existing system by a new one. 相似文献
492.
Das Subhasis Kumar Shit Pravat Bera Biswajit Adhikary Partha Pratim 《Urban Ecosystems》2022,25(5):1541-1559
Urban Ecosystems - Urbanization has profound influence on the changes of land use and land cover, which on the other hand exert significant impact on ecosystem services and their values, especially... 相似文献
493.
Lal Sumeet Singh Rup Chand Ronal Patel Arvind Jain Devendra Kumar 《Journal of Population Research》2022,39(2):257-277
Journal of Population Research - The paper projects aggregate populations of six Pacific Island countries in both pre- and post-COVID19 scenarios using a Cohort Component Method for the period... 相似文献
494.
Tanujit Chakraborty Gauri Kamat Ashis Kumar Chakraborty 《Australian & New Zealand Journal of Statistics》2023,65(2):101-126
Frequentist and Bayesian methods differ in many aspects but share some basic optimal properties. In real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable depending on some subjective criteria. Nonparametric classification and regression techniques, such as decision trees and neural networks, have both frequentist (classification and regression trees (CARTs) and artificial neural networks) as well as Bayesian counterparts (Bayesian CART and Bayesian neural networks) to learning from data. In this paper, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. BNT models can simultaneously perform feature selection and prediction, are highly flexible, and generalise well in settings with limited training observations. We study the statistical consistency of the proposed approaches and derive the optimal value of a vital model parameter. The excellent performance of the newly proposed BNT models is shown using simulation studies. We also provide some illustrative examples using a wide variety of standard regression datasets from a public available machine learning repository to show the superiority of the proposed models in comparison to popularly used Bayesian CART and Bayesian neural network models. 相似文献