Researchers at the University of Tübingen are studying crows’ abilities to understand statistical…Tags: Ars Technica, birds, probability

Katherine Sayre, for The Wall Street Journal, on Las Vegas casinos squeezing out…Tags: gambling, Las Vegas, probability

There was a government-run lottery in the Philippines with a $4 million jackpot,…Tags: lottery, probability

Introduction to Probability for Data Science is a free-to-download book by Purdue statistics…Tags: book, probability, Stanley Chan

With tonight’s Mega Millions jackpot estimated at $1.28 billion, you might be wondering…Tags: lottery, probability

Zack Capozzi, for USA Lacrosse Magazine, explains how he calculates win probabilities pre-game…Tags: probability, sports, Zack Capozzi

Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms. These are sub-fields of machine learning that a machine learning practitioner does not need to know in great depth in order to achieve good results on a wide range of problems. Nevertheless, it …

Learn the fundamentals of conditional probability in R with this interactive statistics course. Master Naive Bayes and learn to build a spam filter with R! The post New Statistics Course: Conditional Probability in R appeared first on Dataquest.

Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, providing a method that is easy to use for scenarios where our intuition often fails. The best way to develop an intuition for Bayes Theorem is to think about the meaning of the …

The Bayes Optimal Classifier is a probabilistic model that makes the most probable prediction for a new example. It is described using the Bayes Theorem that provides a principled way for calculating a conditional probability. It is also closely related to the Maximum a Posteriori: a probabilistic framework referred to …

Inpredictable, a sports analytics site by Michael Beuoy, tracks win probabilities of NBA…Tags: basketball, probability

An empirical distribution function provides a way to model and sample cumulative probabilities for a data sample that does not fit a standard probability distribution. As such, it is sometimes called the empirical cumulative distribution function, or ECDF for short. In this tutorial, you will discover the empirical probability distribution …

Introduction to Probability by Joseph Blitzstein and Jessica Hwang is available as a free PDF download. The book contains: Distributions Random Variables Markov Chains Monte Carlo All the background Math Code Examples in R and lots more….

The behavior and performance of many machine learning algorithms are referred to as stochastic. Stochastic refers to a variable process where the outcome involves some randomness and has some uncertainty. It is a mathematical term and is closely related to “randomness” and “probabilistic” and can be contrasted to the idea …

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically, estimating the entire distribution is intractable, and instead, we are happy to have the expected value of the distribution, such as the mean or mode. Maximum a Posteriori or MAP …

Probabilistic inference involves estimating an expected value or density using a probabilistic model. Often, directly inferring values is not tractable with probabilistic models, and instead, approximation methods must be used. Markov Chain Monte Carlo sampling provides a class of algorithms for systematic random sampling from high-dimensional probability distributions. Unlike Monte …

Monte Carlo methods are a class of techniques for randomly sampling a probability distribution. There are many problem domains where describing or estimating the probability distribution is relatively straightforward, but calculating a desired quantity is intractable. This may be due to many reasons, such as the stochastic nature of the …

Maximum likelihood estimation is an approach to density estimation for a dataset by searching across probability distributions and their parameters. It is a general and effective approach that underlies many machine learning algorithms, although it requires that the training dataset is complete, e.g. all relevant interacting random variables are present. …