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BayesiaLab Webinar Series

Black Swans & Bayesian Networks

Recorded on July 19, 2019

 

Webinar Materials

Abstract

Rare events are typically difficult to model due to the lack of historical data. In fact, the events we are typically concerned about, e.g., catastrophic events, may never have happened before, and will hopefully never ever happen. On the other hand, frequent occurrences can be easily characterized by statistical models learned from data. Inevitably, such statistical models are bound to give us a false sense of security about never-seen-before situations.

Even though we may not have any actual observations, we can still speculate and hypothesize about possible rare events, i.e., we can reason on theoretical grounds as to what could possibly go wrong.
The objective of this webinar is to present Bayesian networks as a framework to merge machine-learned knowledge from data with theoretical knowledge from domain experts in order to produce a joint probability distribution that includes common and rare events at the same time.

The case study we present is addressing some of the challenges of Modern Portfolio Theory and was inspired by Rebonato & Denev's book, Portfolio Management Under Stress.

BayesiaLab Courses

July 17, 2019 Washington, D.C. BayesiaLab 101 Short Course (1 Day)
September 18–20, 2019 Paris, France Introductory Course (3 Days)
September 23–25, 2019 Paris, France Advanced Course (3 Days)
October 7–9, 2019 Durham, NC Introductory Course (3 Days)
October 14–16, 2019 Durham, NC Advanced Course (3 Days)

Upcoming Seminars, Webinars, and Conferences

Live Webinar July 19, 2019 11:00 – 12:00 (CDT, UTC-5) Black Swans & Bayesian Networks — Jointly Representing Common and Rare Events
Please check out our archive of recordings of previous events.

7th Annual BayesiaLab Conference

October 7–9, 2019 Durham, NC 3-Day Introductory Course
October 10–11, 2019 Durham, NC 7th Annual BayesiaLab Conference
October 14–16, 2019 Durham, NC 3-Day Advanced Course