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Why we need more Bayesian trained data scientists than frequentist post COVID 19 ..

Earlier this week, I was speaking at an event on AI for Real Estate where I showed an example from a BBC clip which said that “central London is now a ghost town” (due to COVID 19)

A few months ago, this headline would have been laughable

In London, central London and the London underground are a key fabric of daily life

So, my hypothesis to this conference was:

  • In a post COVID world, the past does not equal the future
  • frequentist approaches rely on data (past)
  • In many cases (ex: Real estate), now that does not hold true and hence, we need new approaches
  • Specifically, we need to look at Bayesian approaches which are not so common in the current curricula of data science and in practitioners

To expand on the reasons,

  • Typically, data scientists lack the data to model a process well. Now, the situation has got worse because the data we have (if we could get hold of enough of it in the first place) – may not hold true anymore because we face a discontinuity
  • Bayesian techniques allow us to encode expert knowledge easier
  • Bayesian techniques perform better with sparse data
  • Bayesian models are more easily interpretable
  • Bayesian techniques allow for smaller datasets
  • You could combine Bayesian techniques with other models like Hidden Markov models
  • Bayesian techniques allow you to model rare/non-repeatable events

All these characteristics have become relevant today 

In any case, it’s a viewpoint that needs greater emphasis in both practice and education.

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