AutoBayes-Automatic Synthesis of Statistical Data Analysis Programs from Bayesian Networks

摘要

The goal of the AutoBayes project is to make statistical data analysis easier and more accessible to scientists by automatically synthesizing efficient data-analysis programs from statistical models that are used for the definition of valid information. Data analysis can be defined as any process that extracts more abstract information from mere data. It includes such diverse tasks as general parameter estimation and curve fitting, clustering and classification, data compression, fusion of heterogeneous data sources, change and anomaly detection in time-series or image data, or image segmentation. Although there are many approaches to data analysis, statistical data analysis is the only mathematically rigorous approach. In statistical data analysis, a statistical model is used to define how much information the data originally contains, and thus, how much statistically valid information can ultimately be extracted from the data. This approach is standard in medical sciences such as epidemiology, where the cost of wrong conclusions can be high, and statistical data analysis is now becoming more widespread within the fields relevant to NASA. Unfortunately, the development of statistical data-analysis programs is expensive and time-consuming, and it requires expertise at the intersection of computer science, statistics, and the application.