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.

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.

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Jay Jacobs has some fun with roulette simulations and explores the odds of winning for different bets. Above shows a simulation of 250 spins 20,000 times. Or to put it differently, it's like simulating the play of 20,000 people, who each took 250 spins and always bet on a single number.

I'm not sure why it doesn't start to get red until you're $500 in the hole, but bottom line: the longer you play, the higher probability you will lose all your money. That was my main takeaway from Probability 101 in undergrad. The rest is a blur.

Every website needs an audience. And every audience needs a goal. Advocating for end-user needs is the very foundation of the user experience disciplines. We make websites for real people. Those real people are able to do real things. But how do we get to really know our audience and find out what these mystery users really want from our sites and applications? Learn to ensure that every piece of content on your site relates back to a specific, desired outcome — one that achieves business goals by serving the end user. Corey Vilhauer explains the threads that bind UX research to content strategy and project deliverables that deliver.

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