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Bayesian statistics is a type of dynamic probability statistics commonly used in today’s world of artificial intelligence and machine learning. These technologies seek to go beyond pure linear programming to a more probabilistic approach. Bayesian statistics complements this idea, because a Bayesian statistical approach is more sophisticated and based on a different probabilistic foundation than “frequentist” statistics that have been the most common type of statistical analysis done to date.
Frequentist statistics simply take the probability of a given event based on known test sets of a specific number. By contrast, Bayesian statistics take probability and allow it to express a “degree of belief” in an outcome, and establish reasoning based on hypotheses. Bayesian statistics was first pioneered in the 1770s by Thomas Bayes, who created the Bayes theorem that puts these ideas to work.
Another way to think about Bayesian statistics is that it utilizes “conditional probabilities” – it takes multiple factors into account. Think about the coin toss, where one can run large numbers of tests to determine that the frequentist statistical model is going to be close to 50 percent every time. However, Bayesian statistics might take conditional factors and apply them to that original frequentist statistic. What if one factored in whether or not it was raining when identifying the outcome of the coin toss? Might that affect the outcomes in terms of statistical results?
As a rule, environmental factors like that would not change the outcome of the coin toss – but in the business world, where so many conditional factors affect each other, Bayesian statistics can be a powerful part of getting insights out of data. That is the reason Bayesian statistics are so commonly used in enterprise technologies.
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