Acceptance Sampling models have been widely applied in companies for the inspection and testing the raw material as well as the final products. A number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. The acceptance sampling models only provide the guarantee for the producer and consumer that the items in the lots are acco...

Bayesian network are widely accepted as models for reasoning with uncertainty. In this chapter we focus on models that are created using domain expertise only. After a short review of Bayesian networks models and common Bayesian network modeling approaches, we will discuss in more detail three applications of Bayesian networks. With these applications, we aim to illustrate the modeling power an...

Interdependencies of ecologic, hydrologic, and social systems challenge traditional approaches to natural resource management in semi-arid regions. As a complex social-ecological system, water demands in the Sonoran Desert from agricultural and urban users often conflicts with water needs for its ecologically-significant riparian corridors. To explore this system, we developed an agent-based mo...

Second-order uncertainty, also known as model uncertainty and Knightian uncertainty, arises when decision-makers can (partly) model the parameters of their decision problems. It is widely believed that subjective probability, and more generally Bayesian theory, are illsuited to represent a number of interesting second-order uncertainty features, especially “ignorance” and “ambiguity”. This fail...

The bigram language models are popular, in much language processing applications, in both Indo-European and Asian languages. However, when the language model for Chinese is applied in a novel domain, the accuracy is reduced significantly, from 96% to 78% in our evaluation. We apply pattern recognition techniques (i.e. Bayesian, decision tree and neural network classifiers) to discover language ...