{"id":248894,"date":"2025-05-20T10:17:30","date_gmt":"2025-05-20T10:17:30","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/what-is-bayesian-thinking\/"},"modified":"2025-05-20T10:17:30","modified_gmt":"2025-05-20T10:17:30","slug":"what-is-bayesian-thinking","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=248894","title":{"rendered":"What is Bayesian Thinking?"},"content":{"rendered":"


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As students, we often ponder how our results will be after the final term examinations. So, we start speculating based on our previous internal marks performance, the number of all-nighters we have pulled, and our prior performance in similar courses. This approach of updating our beliefs about our potential performance aligns very closely with a powerful statistical framework known as \u201cBayesian Thinking\u201d. This technique adopts the logic of Bayesian theorem which we know in machine learning as the Bayes formula. You might\u2019ve never quite realized it, but most of our introspection regarding the future is heavily dependent on Bayes\u2019 conditional probability<\/a>. In this article, we will dive deeper into how we can correlate Bayesian thinking with our daily life to formalize and improve our estimations of future outcomes.<\/span><\/p>\n

Core of Bayesian Thinking<\/span><\/h2>\n

Bayesian thinking, as the name suggests, is based on the Bayes Theorem, which predominantly follows these 3 fundamental concepts \u2013 prior, likelihood, and posterior. Let\u2019s understand them based on the example of gauging our final exam performance.<\/span><\/p>\n

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  1. Prior:<\/b> The initial belief we have upon an uncertain (e.g., the probability you\u2019ll get an A on the final exam) before seeing new data.<\/span><\/li>\n
  2. Likelihood:<\/b> The probability of understanding new data given a particular hypothesis (e.g., how likely we are going to score well in the final exam if we study x hours per day).<\/span><\/li>\n
  3. Posterior:<\/b> The updated belief we have when a new situation occurs, which is calculated using Bayes\u2019 Theorem.<\/span><\/li>\n<\/ol>\n
    \"Bayes
    Source \u2013 <\/span>Medium<\/span><\/a><\/figcaption><\/figure>\n

    Here, for 2 events A and B:<\/span><\/p>\n

    P(A) is the prior probability of hypothesis B.<\/span>
    P(B|A) is the likelihood of data B given A.<\/span>
    P(B) is the marginal probability of data B.<\/span>
    P(A|B) is the posterior probability of A after observing B.<\/span><\/p>\n

    So, in our exam-based scenario:<\/span><\/p>\n