This research investigates how the real options framework and Bayesian decision theory may be utilized to improve the capital budgeting decision process. In particular, it investigates the theoretical and modeling advantage of merging option pricing theory with the Bayesian revision process to value investment decisions defined by partial or full irreversibility of capital outlays, uncertainty, and the opportunity to gather information. Benchmarking from existing real options and Bayesian approaches, new modeling methodologies are developed that value delay investment scenarios in the context of information acquisition and inclusion in the decision process. In this context, real option attributes are discussed from a statistical decision theoretic perspective, thresholds are identified for improved decision-making, information's impact on downstream decision-making is formally defined, and project activation policies are developed. Using real data provided by firms in the aerospace maintenance, repair, and overhaul industry, this Bayesian Learning Real Options (BLRO) methodology is demonstrated within contingent investment and license valuation scenarios.