Statistics

In this text, I'll introduce you to hierarchical Bayesian (HB) modelling, a versatile approach to mechanically mix the outcomes of multiple sub-models. This method enables estimation of individual-level effects by optimally combining information across...

as an NBA coach? How long does a typical coach last? And does their coaching background play any part in predicting success? This evaluation was inspired by several key theories. First, there was a...

generally is a scary topic for people. A lot of you must work in machine learning, however the maths skills needed could seem overwhelming. I'm here to inform you that it’s nowhere as intimidating as...

-up to my earlier article: The Dangers of Deceptive Data–Confusing Charts and Misleading Headlines. My first article focused on how will be used to mislead, diving right into a form of knowledge presentation...

distributions are essentially the most commonly used, numerous real-world data unfortunately will not be normal. When faced with extremely skewed data, it’s tempting for us to utilize log transformations to normalize the distribution...

In models, the independent variables have to be not or only barely depending on one another, i.e. that they are usually not correlated. Nevertheless, if such a dependency exists, that is known as...

Introduction Parameter estimation has been for a long time one of the crucial necessary topics in statistics. While frequentist approaches, akin to Maximum Likelihood Estimations, was once the gold standard, the advance of computation...

truth isn't perfect. From scientific measurements to human annotations used to coach deep learning models, ground truth all the time has some amount of errors. ImageNet, arguably essentially the most well-curated image dataset...