Bayesian statistics: a comprehensive course This playlist provides a complete introduction to the field of Bayesian statistics. It assumes very little prior knowledge and, in particular, aims to provide explanations of concepts with as little maths as possible. The course covers the following topics: probability distributions, marginal and conditional probability, the Bayesian formula, the difference between Bayesian and Frequentist statistics, Likelihood, how to specify a prior, the probability of data given model choice, an introduction to the probability distributions commonly used in Bayesian data analysis, conjugate priors, credible intervals, highest density posterior intervals, Objective Bayesian data analysis, Jeffrey's prior, Reference priors, Zellners's G-priors, forecasting in Bayesian systems, Markov Chain Monte Carlo, grid approximations, Metropolis-Hastings sampling, Gibbs sampling, hypothesis testing: classical test analogues and pure Bayesian methods, hierarchical models, hyperpriors, linear regression.
Adorably Rotund Ginger Invades Iconic Paintings to Turn Them into Cat Art ‘Mona Lisa' by Leonardo da VinciThis post may contain affiliate links. If you make a purchase, My Modern Met may earn an affiliate commission. Please read our disclosure for more info. Artist Svetlana Petrova finds a creative muse (or “mews”) in her ginger cat, Zarathustra, and the adorable fat feline has made its way into some of the most well-known works of art history. With crazed cat eyes, a fluffy belly, and a penchant for flowers, the creature seamlessly invades paintings to add his own dose of beauty while giving new meaning to them.
Introduction to Data Science with R - O’Reilly Media Learn practical skills for visualizing, transforming, and modeling data in R. This comprehensive video course shows you how to explore and understand data, as well as how to build linear and non-linear models in the R language and environment. It’s ideal whether you’re a non-programmer with no data science experience, or a data scientist switching to R from other software such as SAS or Excel. RStudio Master Instructor Garrett Grolemund covers the three skill sets of data science: computer programming (with R), manipulating data sets (including loading, cleaning, and visualizing data), and modeling data with statistical methods. Statistics Using Technology I hope you find this book useful in teaching statistics. When writing this book, I tried to follow the GAISE Standards (2014, January 05), which are: Emphasis statistical literacy and develop statistical understanding.Use real data.Stress conceptual understanding, rather than mere knowledge of procedure.Foster active learning in the classroom.Use technology for developing concepts and analyzing data. To this end, I ask students to interpret the results of their calculations.
Woman Instituted Ministry of Silly Walks for Her Neighbors & They Do It As the weeks spent in coronavirus quarantine go on, people are finding new (and usual) ways to entertain themselves, whether it’s recreating iconic art or giving a dog a daily makeover. Liz Koto and her family have found another way to make us laugh—with some help from their neighbors. They have deemed the sidewalk in front of their house as the “jurisdiction of the Ministry of Silly Walks” and instructed those who pass by their sign to immediately begin “silly walking.” All of the resulting funny footwork is captured on the family’s doorbell cam.
LeaRning Path on R - Step by Step Guide to Learn Data Science on R One of the common problems people face in learning R is lack of a structured path. They don’t know, from where to start, how to proceed, which track to choose? Though, there is an overload of good free resources available on the Internet, this could be overwhelming as well as confusing at the same time. To create this R learning path, Analytics Vidhya and DataCamp sat together and selected a comprehensive set of resources to help you learn R from scratch. This learning path is a great introduction for anyone new to data science or R, and if you are a more experienced R user you will be updated on some of the latest advancements. Collaborative Statistics Have you heard others say, “You’re taking statistics? That’s the hardest course I ever took!” They say that, because they probably spent the entire course confused and struggling. They were probably lectured to and never had the chance to experience the subject.
Bored Quarantined Owners Have Started Putting Cats In Cardboard Tanks What happens when you have lots of free time and need to distract yourself from uncontrollable life events? You unleash your creativity! We have already featured People Recreating Famous Paintings, Guinea Pig Art Museum, and Reenact Movie Scenes.
HyperStat Online: An Introductory Statistics Textbook and Discussion of whether most published research is false Recommend HyperStat to your friends on Facebook Click here for more cartoons by Ben Shabad. Other Sources Stat Primer by Bud Gerstman of San Jose State University Statistical forecasting notes by Robert Nau of Duke University related: RegressIt Excel add-in by Robert Nau CADDIS Volume 4: Data Analysis (EPA) The little handbook of statistical practice by Gerard E.
A geometric interpretation of the covariance matrix Introduction In this article, we provide an intuitive, geometric interpretation of the covariance matrix, by exploring the relation between linear transformations and the resulting data covariance. Most textbooks explain the shape of data based on the concept of covariance matrices. Instead, we take a backwards approach and explain the concept of covariance matrices based on the shape of data. In a previous article, we discussed the concept of variance, and provided a derivation and proof of the well known formula to estimate the sample variance. Figure 1 was used in this article to show that the standard deviation, as the square root of the variance, provides a measure of how much the data is spread across the feature space.
Cats Wearing Hats Made From Their Own Hair Japanese photographer Ryo Yamazaki makes hats from the hair his cats shed, and they are beautiful! Scroll down to see his marvellous creations! Weka 3 - Data Mining with Open Source Machine Learning Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this.