This dissertation describes a new integrated uncertainty analysis methodology for "best estimate" thermal hydraulics (TH) codes such as RELAP5. The main thrust of the methodology is to utilize all available types of data and information in an effective way to identify important sources of uncertainty and to assess the magnitude of their impact on the uncertainty of the TH code output measures. The proposed methodology is fully quantitative and uses the Bayesian approach for quantifying the uncertainties in the predictions of TH codes. The methodology also uses the data and information for a more informed and evidence-based ranking and selection of TH phenomena through a modified PIRT method. The modification considers importance of various TH phenomena as well as their uncertainty importance. In identifying and assessing uncertainties, the proposed methodology treats the TH code as a white box, thus explicitly treating internal sub-model uncertainties, and propagation of such model uncertainties through the code structure as well as various input parameters. A The TH code output is further corrected through a Bayesian updating with available experimental data from integrated test facilities. It utilizes the data directly or indirectly related to the code output to account implicitly for missed/screened out sources of uncertainties. The proposed methodology uses an efficient Monte Carlo sampling technique for the propagation of uncertainty using modified Wilks sampling criteria. The methodology is demonstrated on the LOFT facility for 200% cold leg LBLOCA transient scenario.