One of the earliest quantitative tools adopted by Western intelligence services to arrive at better estimates of scenarios materializing based on a stream of evidence is Bayes’ theorem, sometimes also known as the inverse probability method. In this module, we will provide a hands-on tutorial on Bayesian reasoning drawing from real-world cases. Along the way, we will conceptually introduce some basic notions of probability theory and the importance of updating beliefs while making inferences. We will conclude with detailed how-to instructions which can be followed by analysts working individually or in groups to arrive at much more accurate forecasts than those based on pure subjective judgement.
- Quantifying intelligence judgement
- Probability, conditional probability, and subjective probability
- The problem of evidence and priors
- Bayes’ theorem through basic examples
- Real-life illustrations and application methods
The module consists of three one-hour live lectures along with time for interactive exercises. It can be delivered in-person or virtually.
About the Instructor
Abhijnan REJ is a security analyst and mathematician, and Founder & Chief Scientist of Tarqeq Research LLP. He has held senior positions in think tanks and media, and has also been a corporate R&D scientist and mathematics lecturer. Rej holds undergraduate and graduate degrees in mathematics from the University of Connecticut.
Tarqeq Research LLP provides bespoke research-based solutions and training to help meet a wide range of global security challenges of concern to businesses, national and subnational governments using cutting-edge methods steeped in contemporary scientific traditions.