Bayes Theorem is a complex mathematical construct that originated in the 18th century. It was the work of Thomas Bayes, a minister and statistician who published a paper called “An Essay Towards Solving a Problem in the Doctrine of Chances”. Despite sounding somewhat arcane, the theorem, and the principle of ‘Bayesian Probability’, has played a practical role in decision-making at various points in history, and can now be used by modern businesses.
The impact of the Bayes Theorem formula
The impact of the work carried out during World War II by Alan Turing when cracking the Nazi Enigma code is well known, and is generally acknowledged as having played a major role in defeating the Nazis. What is less well known is that Turing and his team made use of Bayesian probability models to reduce the potentially infinite number of possible ways to crack the messages in question, leaving those that were most likely to be translated. In doing so they managed to break what was thought to be an unbreakable code.
Bayes Theorem explained
In simple terms, Bayes Theorem is a mathematical model that uses the power of statistics and probability to calculate the likelihood of a specific scenario or set of events when compared to an alternative scenario. It works around something called “conditional probability”, which is the chance of something specific happening when other scenarios are taken into account.
For example, the chances of a person completing a round of golf within a set period of time will depend upon factors such as:
How long previous rounds of golf have taken to complete
The nature of the golf course itself
The number of other people playing the same round
How often the golf ball is struck
Another simple example consists of a dinner party guest announcing that they are going to bring a friend with them to the meal. On the basis of this information, the probability of the guest being male or female would be set at 50% in both cases. If the dinner party guest then messages you with a comment about how you surely remember their friend with the crew cut and big muscles, this extra information then shifts the probability toward the extra guest being a man (this being because we are more likely to associate these physical attributes with male bodies).
Bayes Theorem examples in business and finance
Bayes Theorem uses events to calculate future probability, and although the actual Bayes Theorem formula, when written out, is pretty much meaningless to anyone who is not an experienced and highly qualified mathematician, the principles it encompasses are relatively simple and can be applied to the realms of business and finance:
Interest rates – an unexpected shift in interest rates that aren’t fixed can have an impact on a business, negatively affecting the amount having to be paid on some loans and the process of exporting to overseas territories with different currencies. Bayes Theorem enables a business to estimate the probability of such a shift happening and factor the likely changes into its financial planning.
Net income – net income, representing the profit a business generates, can be affected dramatically by external sources, covering anything from the weather to geopolitical occurrences and a shift in the cost of materials. Probability scenarios can be applied when events such as this take place in order to calculate the likely impact, making it easier to take corrective action.
Insurance – in a real-world example, Bayes Theorem is used by insurance companies to calculate the likelihood of events such as floods taking place.
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