Name: Rocket Science meets Bayesian Models: Space risks - small binary data
Start: 2023-03-21T18:00:00Z
End: 2023-03-21T19:00:00Z
Location: Bayes Business School

Abstract:
Space launch vehicles are high risk items with binary outcomes. However, the numbers of launch attempts are limited and so we have a small data set with which to work. Additionally, the heritage of a particular launch vehicle design can span many decades and the risk is far from constant. We will be looking at how I have tackled this problem, solutions to some of the problems encountered and the test and evaluation methodology applied. These issues and solutions are not space risk specific but have generalised application to many binary outcome problems.

Markus Gesmann

Bayesian Mixer London

Technology

Statistical Computing

Bayesian Statistics

Applied Statistics

Statistical Science

Statistical Modeling

Neil’s current role is as the Space Risk Analyst at Ascot, a large underwriter in Lloyds. His background covers a diverse range of roles. He has worked with Space insurance since 2005 but has also produced models to evaluate the risks for UK property (both primary & reinsurance), mining, power generation (wind, fossil fuel and solar) and General Aviation. He works predominately in R and has used Bayesian Hierarchical Models, Boosted Trees, NLP, optimisation and is currently working with graph databases. He holds engineering master degrees from both Churchill College, Cambridge and Clemson University and an MBA from Said Business School, Oxford.

Neil Fleming, Space Risk Analyst

Rocket Science meets Bayesian Models: Space risks - small binary data

Bayes Business School

Share

Bayesian Mixer London

Rocket Science meets Bayesian Models: Space risks - small binary data

Bayesian Mixer London

Details

Members are also interested in