Knowledge database

In August 2014, Omar Gonzalez, a US war veteran who had been wounded in Iraq and psychologically affected by his experience, managed to enter the White House with a knife evading all security. In this white paper, we look at how all the security measures could have failed at the same time.

Add, subtract, multiply, divide - we have learned how to use them with numbers at school by the age of ten at the latest, and we take it for granted that we have mastered them. It is hard to imagine a risk analysis model in any field that does not include some of these four operations. Yet these basic operations very often do not work in the same way when those numbers are uncertain. Very worryingly, nearly every person we encounter who is involved in risk modeling is to some degree unaware or unclear about the correct ways of manipulating uncertain variables in a model, perhaps because we don't give a second thought to calculations using + - * /. In this white paper we'll have a closer look at calculation with probability distributions.

A common problem in risk analysis is fitting a probability distribution to a set of observations for a variable. One does this to be able to make forecasts about the future. The most common situation is to fit a distribution to a single variable (like the lifetime of a mechanical or electrical component), but problems also sometimes require the fitting of a multivariate distribution: for example, if one wishes to predict the weight and height of a random person, or the simultaneous change in price of two stocks. There are a number of software tools on the market that will fit distributions to a data set, and most risk analysis tools incorporate a component that will do this. Unfortunately, the methods they use to measure the goodness of fit are wrong and very limited in the types of data that they can use. This paper explains why, and describes a method that is both correct and sufficiently flexible to handle any type of data set.