Even if you are not a Bayesian Stats fan, it’s not hard to appreciate this data analytics framework. In fact, it would irresponsible if you were to disregard it without delving into it, at least to some extent. Nevertheless, the fact is that Frequentist Stats (see image above), as well as Machine Learning, are more popular in data science. Let's explore the reasons why this is. Bayesian Stats relies primarily on the various versions of the Bayes Theorem. In a nutshell, this theorem states that if we have some idea of the a priori probabilities of an event A happening (as well as A not happening), as well as the likelihoods of event B happening given event A happening (as well as A not happening), we can estimate the probability of A given B. This is useful in a variety of cases, particularly when we don't have a great deal of data at our disposal. However, there is something often hard to gauge and it's the Achilles heel of Bayesian Stats. Namely, the a priori probabilities of A (aka the priors) are not always known while when they are, they are usually rough estimates. Of course, this isn't a showstopper for a Bayesian Stats analysis, but it is a weak point that many people are not comfortable with since it introduces an element of subjectivity to the whole analysis. In Frequentist Stats, there are no priors and the whole framework has an objective approach to things. This may seem a bit farfetched at times since lots of assumptions are often made but at least most people are comfortable with these assumptions. In Machine Learning, the number of assumptions is significantly smaller as it's a datadriven approach to analytics, making things easier in many ways. Another matter that makes Bayesian Stats not preferable for many people is the lack of proper education around this subject. Although it predates Frequentist Stats, Bayesian Stats never got enough traction in people's minds. The fact that Frequentist Stats was advocated by a very charismatic individual who was also a great data analyst (Ronald A. Fisher) may have contributed to that. Also, the people who embraced the different types of Statistics at the time augmented the frameworks with certain worldviews, making them more like ideological stances than anything else. As a result, since most people who worked in data analytics at the time were more partial towards Fisher's worldview, it made more sense for them to advocate Frequentist Stats. The fact that Thomas Bayes was a man of the cloth may have dissuaded some people from supporting his Statistics framework. Finally, Bayesian Stats involves a lot of advanced math when it is applied to continuous variables. As the latter scenario is quite common in most data analytics projects, Bayesian Stats ends up being a fairly esoteric discipline. The latter entails things like Monte Carlo simulations (which although fairly straightforward, they are not as simple as distribution plots and probability tables) and Markov Chains. Also, there are lots of lesserknown distributions used in Bayesian Stats (e.g. Poisson, Beta, and Gamma, just to name a few) that are not as simple or elegant as the Normal (Gaussian) distribution or the Student (t) distribution that are bread and butter for Frequentist Stats. That's not to say that the latter is a walk in the park, but it's more accessible to a beginner in data analytics. As for Machine Learning, contrary to what many people think, it too is fairly accessible, especially if you use a reliable source such as a course, a book, or even an educational video, etc. with a price tag accompanying it. Summing up, Bayesian Statistics is a great tool that’s worth exploring. If, however, you find that most data analytics professionals don’t share your enthusiasm towards it, don’t be dismayed. This is something natural as the alternative frameworks maintain an advantage over Bayesian Stats.
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I. Archives
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