Abstract: The harsh conditions that will exist in fusion devices that are in full nuclear environments will place severe limits on the diagnostics. Integrated data analysis (IDA) provides methods to maximize the scientific value of the diagnostic data, and is a growing area of research in the fusion community. The goal of IDA is to combine information from different diagnostics to get the most reliable measurement of physical parameters of interest in a standardized and transparent way. A natural framework in which to develop IDA is Bayesian probability theory or Bayesian analysis. On its own, Bayesian analysis of a diagnostic can offer advantages over traditional approaches to analysis, in particular when the analysis involves complex inversions. In the context of IDA, the probabilistic focus of Bayesian analysis allows an IDA technique to be developed modularly, one diagnostic at a time. It automatically includes error analysis and provides a method for including background information into the analysis quantitatively. This talk presents an introduction to a Bayesian approach to data analysis using concrete examples. It focuses on explaining the terms in Bayes’ Rules, the foundation for Bayesian analysis, and will highlight important considerations that need to be made when applying Bayesian analysis to a particular diagnostic. Its function as a framework for IDA will also be illustrated.