PinnedMember-onlyWhy Most Published Research Findings Are FalseThere is growing concern that the majority of research findings that have been published are inaccurate. The chance that a research statement is accurate might be influenced by factors such as the study’s strength and potential for bias, the number of previous studies conducted on the same topic, and, importantly…Reserach2 min readReserach2 min read
PinnedMember-only6 Useful Chrome Extensions for Medium WritersAs a writer on Medium, it’s important to have the right tools at your disposal to help you create high-quality content that resonates with your audience. One of the best ways to enhance your writing experience on Medium is by using Chrome extensions. …Medium Tips5 min readMedium Tips5 min read
PinnedMember-onlyOptimal Timing for Posting on MediumMedium is a popular platform for writers, bloggers, and content creators to share their work with a wider audience. With millions of readers visiting the site every day, choosing the right time to publish your content can significantly impact the visibility and engagement of your posts. However, there is no…Medium3 min readMedium3 min read
Jul 28Member-onlyUnderstanding Independent and Identically Distributed (i.i.d.) Data in StatisticsIn the field of statistics, “Independent and Identically Distributed” (i.i.d.) is a fundamental concept that underpins many statistical methods and models. Whether you are exploring data, performing hypothesis testing, or building machine learning algorithms, understanding i.i.d. assumptions is crucial for drawing meaningful conclusions and making accurate predictions. In this blog…Statistics4 min readStatistics4 min read
Jul 24Member-onlyIntroducing Markov Chain Monte Carlo: A Powerful Tool for Simulations and BeyondIn the world of statistical simulations and data analysis, Markov Chain Monte Carlo (MCMC) has emerged as a powerful and versatile technique. Initially developed in the 1940s, MCMC gained significant traction in the last few decades as computational power increased. Its applications span across various fields, including physics, computer science…Data Science4 min readData Science4 min read
Jul 24Member-onlyIntroduction to Gibbs samplingGibbs sampling is a Markov Chain Monte Carlo (MCMC) technique used for statistical inference and sampling from complex probability distributions, especially in Bayesian statistics. It was proposed by Josiah Willard Gibbs, a physicist and mathematician, in the late 19th century. The main goal of Gibbs sampling is to approximate the…4 min read4 min read
Jul 22Member-onlyUnderstanding Maximum Likelihood Estimation (MLE) in Machine LearningMaximum Likelihood Estimation (MLE) is a fundamental concept in machine learning and statistics that plays a crucial role in parameter estimation for probabilistic models. It forms the backbone of many algorithms and techniques used in modern data-driven applications. In this blog, we will explore the concept of Maximum Likelihood Estimation…Maximum Likelihood4 min readMaximum Likelihood4 min read
Jul 22Member-onlyUnderstanding Maximum a Posteriori (MAP) Estimation in Machine LearningIn the field of machine learning, making accurate predictions from data is crucial for building effective models. One common challenge is estimating model parameters when dealing with limited data or uncertain knowledge about the parameter values. Maximum a Posteriori (MAP) estimation comes to the rescue by providing a Bayesian approach…Maximum A Posteriori3 min readMaximum A Posteriori3 min read
Jul 21Member-onlyMarginalization in Gaussian DistributionMarginalization is a fundamental operation in probability and statistics that involves integrating out or summing over a variable to obtain the probability distribution of the remaining variables. When dealing with Gaussian distributions, marginalization is a straightforward process because the Gaussian distribution is closed under marginalization and conditioning operations. Let’s consider…Gaussian Distribution2 min readGaussian Distribution2 min read
Jun 22Member-onlySinkhorn Knopp: Unraveling Optimal Transport for Data AlignmentIn the realm of data alignment and optimal transport, the Sinkhorn Knopp algorithm has emerged as a powerful tool for solving transportation optimization problems. With applications ranging from image matching to text alignment and network analysis, Sinkhorn Knopp offers an elegant solution to the challenging task of finding optimal mappings…Data Science4 min readData Science4 min read
