ABSTRACT
Confidence Interval Evaluation . . . . . . . . . . . . . . . . . . . . . . 313 8.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316
The analysis of data is a key part of any scientific endeavor, as it leads to a better understanding of the world around us. With scientific data now being measured in terabytes and petabytes, this analysis is becoming quite challenging. In addition, the complexity of the data is increasing as well due to several factors such as improved sensor technologies and increased computing power. This complexity can take various forms such as multisensor, multispectral, multiresolution data, spatio-temporal data, high-dimensional data, structured and unstructured mesh data from simulations, data contaminated with different types of noise, three-dimensional data, and so on.
