Creator of BayesAIn Strategies
Dr. Luke Miller is a finance professor and consultant with nearly 30 years of experience developing Bayesian learning algorithms for complex decision-making systems across a wide range of industries.
His work applies a proprietary probabilistic framework — grounded in Bayesian inference — to help organizations navigate uncertainty, evaluate risk, and improve strategic outcomes.
Dr. Miller’s consulting practice spans a broad cross-section of industries, with particular depth in financial services. He has advised institutions such as Morningstar, Neuberger Berman, American Express, Ventas, and StanCorp Financial Group, along with numerous hedge funds, family offices, and nonprofit endowments.
Dr. Miller currently serves as a finance professor in the Economics & Business Department at Saint Anselm College.
As Featured In
Published Work
And Its Application to Capital Budgeting Problems
VDM Verlag · 2010
This book presents a rigorous Bayesian learning framework for evaluating capital investment decisions under uncertainty. Rooted in Dr. Miller’s doctoral research, it develops a probabilistic methodology that dynamically incorporates new information into real options valuations — providing decision-makers with a principled approach to investment timing, deferral, and abandonment across industries including financial services, aerospace, and operations management.
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Working papers and peer-reviewed publications.
A Bayesian Real Options framework for capital budgeting decisions defined by irreversibility, uncertainty, and the opportunity to gather information before committing.
A Bayesian Learning Real Options model integrating Bayesian decision-making with real options pricing, applied to valuing a parts manufacturing license in aerospace.
Real options analysis applied to investment decisions within aerospace Maintenance, Repair, and Overhaul, treating equipment upgrades as sequential choices under demand uncertainty.
A survey examining real options analysis and its advantages over conventional DCF approaches, with attention to managerial adaptability and future research directions.