Andrei Kolmogorov is a name unfamiliar to most, but his work had lasting impact. Slava Gerovitch profiled the mathematician, describing the change in thought towards probability theory, which was once more of a joke than a serious approach to evaluate the world. I especially liked the bit about Kolmogorov's appreciation for the arts.

Music and literature were deeply important to Kolmogorov, who believed he could analyze them probabilistically to gain insight into the inner workings of the human mind. He was a cultural elitist who believed in a hierarchy of artistic values. At the pinnacle were the writings of Goethe, Pushkin, and Thomas Mann, alongside the compositions of Bach, Vivaldi, Mozart, and Beethoven—works whose enduring value resembled eternal mathematical truths. Kolmogorov stressed that every true work of art was a unique creation, something unlikely by definition, something outside the realm of simple statistical regularity. "Is it possible to include [Tolstoy's War and Peace] in a reasonable way into the set of 'all possible novels' and further to postulate the existence of a certain probability distribution in this set?” he asked, sarcastically, in a 1965 article.

For this rainy Labor Day, here's an uplifting talk by DataKind founder Jake Porway. He talks data and how it can make a worthwhile difference in areas that could use a change.

Nobody knows what the mathematician Rev. Thomas Bayes looked like, but this is the picture everyone uses. The equation is Bayes' theorem.

Nate Silver, baseball statistician turned political analyst, gained a lot of attention during the 2012 United States elections when he successfully predicted the outcome of the presidential vote in all 50 states. The reason for his success was a statistical method called Bayesian inference, a powerful technique that builds on prior knowledge to estimate the probability of a given event happening.

Bayesian inference grew out of Bayes' theorem, a mathematical result from English clergyman Thomas Bayes, published two years after his death in 1761. In honor of the 250th anniversary of this publication, Bradley Efron examined the question of why Bayes' theorem is not more widely used—and why its use remains controversial among many scientists and statisticians. As he pointed out, the problem lies with blind use of the theorem, in cases where prior knowledge is unavailable or unreliable.

As is often the case, the theorem ascribed to Bayes predates him, and Bayesian inference is more general than what the good reverend worked out in his spare time. However, Bayes' posthumous paper was an important step in the development of probability theory, and so we'll stick with using his name.

Read 6 remaining paragraphs | Comments

- Ars Technica
- Bayes' theorem
- bayesian
- Bayesian inference
- Bayesian statistics
- Bradley Efron
- Empirical Bayes method
- facial recognition
- Frequentist inference
- Prior probability
- Probability theory
- Scientific Method
- Sero 7 Lite
- Statistical forecasting
- Statistical inference
- Statistical theory
- Statistics
- The Guardian
- The Guardian Review
- Thomas Bayes
- Thomas Bayes
- United States

This video clearly describes the distribution of wealth in America using a set of transitioning charts. The graphics are good. The explanation is better.

Analyzing and rationalizing in-game stats in a game spanning multiple genres presents an interesting challenge.

- Algorithm
- Amazon
- basic algorithm
- basic algorithm
- basic data mining techniques
- Bayesian statistics
- Creative Commons
- Data mining
- data mining
- data mining
- data mining techniques
- data mining textbooks
- Naive Bayes classifier
- online version
- Pearson
- Python
- recommendation systems
- Recommender system
- Ron Zacharski
- Statistical classification
- Statistics
- Technology

Stack Exchange

*This Q&A is part of a weekly series of posts highlighting common questions encountered by technophiles and answered by users at Stack Exchange, a free, community-powered network of 90+ Q&A sites.*

Mag20 wants to implement automated testing at his company. Problem is, he's tried several times before, but has failed every time. "Everyone gets excited for the first month or two," he writes. "Then, several months in, people simply stop doing it." But now seems like the right time to try bringing automated testing back to the workplace—Mag20's team of 20 experienced developers are about to embark on a big new project.

How can he finally introduce automated testing at his company?

Read 20 remaining paragraphs | Comments

- Ars Technica
- designs/applications
- Evaluation
- Extreme Programming
- information technology
- Psychometrics
- Software development
- Software testing
- StackExchange
- Statistical hypothesis testing
- Statistics
- Technology Lab
- Technology Lab
- Test
- Test automation
- Test-driven development
- Unit testing
- Unit-testing frameworks for Ruby

- Alina Beygelzimer
- Andy Barto
- Andy Ng
- art algorithms
- art algorithms
- book web site
- Cluster analysis
- Computational neuroscience
- Cybernetics
- Data Science Resources
- David MacKay
- Don Levine
- EM algorithm
- Formal sciences
- free online version
- good clustering algorithm
- good clustering algorithm
- Hunch.net
- Information theory
- Joaquin Quiñonero Candela
- John Langford
- John Myles White
- K-means clustering
- large scale multi-armed bandit systems
- Machine learning
- Machine Learning
- machine learning
- Microsoft
- Multivariate statistics
- New York
- online app
- Reinforcement learning
- Stanford
- Statistics
- Stephen Boyd
- Temporal difference learning
- Unsupervised learning
- Yahoo!