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.