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Bayes' Theorem

Bayes' Theorem
An Intuitive Explanation of Bayes' Theorem Bayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction. Your friends and colleagues are talking about something called "Bayes' Theorem" or "Bayes' Rule", or something called Bayesian reasoning. They sound really enthusiastic about it, too, so you google and find a webpage about Bayes' Theorem and... It's this equation. That's all. So you came here. Why does a mathematical concept generate this strange enthusiasm in its students? Soon you will know. While there are a few existing online explanations of Bayes' Theorem, my experience with trying to introduce people to Bayesian reasoning is that the existing online explanations are too abstract. Or so they claim. And let's begin. Here's a story problem about a situation that doctors often encounter: What do you think the answer is? Next, suppose I told you that most doctors get the same wrong answer on this problem - usually, only around 15% of doctors get it right. E. E.

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Bayes' Rule by Kevin Murphy. Intuition Here is a simple introduction to Bayes' rule from an article in the Economist (9/30/00). "The essence of the Bayesian approach is to provide a mathematical rule explaining how you should change your existing beliefs in the light of new evidence. In other words, it allows scientists to combine new data with their existing knowledge or expertise. The Second Story Of Echo And Narcissus Are you listening closely? This is the story you know: "Narcissus was a man who was so in love with himself that he fell in love with his own reflection.

Web designer's ultimate list of free resources - AcasA Programming Whether you build a site or an app you need to use some resources in your design process: images or fonts, background patterns or color suggestions. Maybe you’re stuck in a rut and need some inspiration? In this article I’ll show you the best free resources to use in your next design. These are the resources I use everyday and I decided to compile them and create this ultimate list of free resources and share it with you. All the resources are indexed for quick access on GitHub, without descriptions or associated pictures.

Unhappy Truckers and Other Algorithmic Problems - Issue 3: In Transit When Bob Santilli, a senior project manager at UPS, was invited in 2009 to his daughter’s fifth grade class on Career Day, he struggled with how to describe exactly what he did for a living. Eventually, he decided he would show the class a travel optimization problem of the kind he worked on, and impress them with how fun and complex it was. The challenge was to choose the most efficient route among six different stops, in a typical suburban-errands itinerary. The class devised their respective routes, then began picking them over. Quantum Machine Learning Singularity from Google, Kurzweil and Dwave ? Dwave's 512 qubit system can speedup the solution of Google's machine learning algorithms by 50,000 times in 25% of the problem cases. This could make it the fastest system for solving Google's problems. Google and Dwave have been working on sparse coding, deep learning and unattended machine learning with Dwave's quantum computer helping to get better and faster results in some cases. Google research discusses the use of quantum computers for AI and machine learning. [Google has ] already developed some quantum machine learning algorithms. In praise of BayesSep 30th 2000 Bayesianism is a controversial but increasingly popular approach to statistics that offers many benefits-although not everyone is persuaded of its validity IT IS not often that a man born 300 years ago suddenly springs back to life. But that is what has happened to the Reverend Thomas Bayes, an 18th-century Presbyterian minister and mathematician-in spirit, at least, if not in body. Over the past decade the value of a statistical method outlined by Bayes in a paper first published in 1763 has become increasingly apparent and has resulted in a blossoming of "Bayesian" methods in scientific fields ranging from archaeology to computing.

Stop Bullshitting Yourself If You Want To Wake Up (From The True Matrix) I’m trying to free your mind Neo. But I can only show you the door. You’re the one who has to walk through it.— Morpheus, The MatrixTweet This I have it all, but I feel a void… I think I fell asleep at the wheel of my life. I’m my very own “Walking Dead.” How you can learn to program: web development resources As promised many moons ago, I’ll sum up what courses, books, and websites have been most helpful to me, as well as some that I’ve heard great things about within the web development community. A quick note on costs: I learned how to program on a budget, so I can certainly empathize with people who don’t want to break the bank without proof that they can get a job doing this stuff when they’re done studying. Most of the resources I list here are relatively cheap and require only a one-time investment.

Inconsistent Mathematics, Reutersvärd, and Buddhism: An Interview with Chris Mortensen Inconsistent Mathematics, Reutersvärd, and Buddhism: An Interview with Chris Mortensen by Michael Lopresto Chris Mortensen is Emeritus Professor of Philosophy at the University of Adelaide. He thinks that the inconsistent hasn't been taken seriously enough in Western philosophy, that the masterpieces of Reutersvärd rub our noses in the inconsistent, and that Western philosophy and Buddhism are complementary. Computerized brain made of GPUs could be the future of artificial intelligence For decades computer scientists have been struggling to design an artificial intelligence sophisticated enough that it could pass for a living being. The fruits of that labor so far are snarky chatbots and systems that can crunch large amounts of data and spit out factoids. Google began working on a new method of replicating neural networks using 1,000 computers tied together. Now, one of the researchers that helped Google do it has laid out the framework for an even better brain model that costs a fraction as much. The key to true AI might be the GPU. Traditional artificial intelligence computing has relied on bundling as many processors together as possible.