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Ritwick Banerjee·Feb 23Jello Shots, Shattered Vases, and Statistics: A Human’s Guide to CausalityConfused about technical jargon used by Data scientists? Here’s something that might help!
Arman Malkhasyan·Sep 17, 2025PDF and CDF Explained with Dalmatians 🐶When we collect data, one of the first questions we ask is:
Unicorn Day·Aug 19, 2025How to Use Likelihood to Catch an Overfitting Imposter 🤥You’ve trained your machine learning model. The training metrics are through the roof — near-perfect accuracy, vanishingly low error…
Jeongwon·Jul 25, 2025Study: Likelihood, MLE, MAP, EMFor my personal study to catch the concept of these.
Shivam Gangwar·Jun 4, 2025Maximum Likelihood Estimation (MLE) in Data ScienceMLE is not just a formula. It’s a philosophy of choosing the most likely parameters given the data.
Jumbong Junior Quantitatif analyst·May 4, 2025Application of the Bayesian PhilosophyNormal Priors and Posteriors
Jumbong Junior Quantitatif analyst·May 3, 2025Application of the Bayesian PhilosophyFrom Uniform Priors to Beta Posteriors
Abhiroop Agarwal·May 3, 2025Bayesian Regression — From ScratchImagine you’re predicting house prices using square footage. A common method is linear regression, which fits a line:
