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RailsConf 2017: Bayes is BAE by Richard Schneeman Before programming, before formal probability there was Bayes. He introduced the notion that multiple uncertain estimates which are related could be combined to form a more certain estimate. It turns out that this extremely simple idea has a profound impact on how we write programs and how we can think about life. The applications range from machine learning and robotics to determining cancer treatments. In this talk we'll take an in depth look at Bayses rule and how it can be applied to solve problems in programming and beyond.
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In this talk titled 'Bayes is BAE' presented by Richard Schneeman at RailsConf 2017, the concept and significance of Bayes' theorem is explored, emphasizing its role in predictions despite uncertainties in data. The presentation begins with a historical context, introducing Thomas Bayes, a Presbyterian minister and statistician who formulated Bayes' rule, a foundational concept in probability and statistical inference. The speaker highlights that while humans are not inherently good at predicting outcomes, the application of Bayes' theorem can improve predictions through combined probabilities from related uncertain estimates. Key points discussed in the video include: - **Introduction to Bayes' Rule**: The theorem enables combining multiple uncertain estimates to form more accurate predictions. - **Inverse Probability**: Understanding how the probability of outcomes can inform the likelihood of an event based on limited data. - **Applications of Bayes' Rule**: Its use in various fields such as artificial intelligence and robotics, where prediction and measurement play crucial roles. - **Robotics Example**: A detailed explanation of a common filter, which uses measurements and predictions to determine a robot's position amidst noisy data, enhancing estimation accuracy. - **Real-World Experiment**: A practical illustration using a rare coin, showcasing how Bayes' theorem can calculate probabilities based on the outcomes of a wager. - **The Importance of Prediction**: Emphasizing that predictions, regardless of their accuracy, guide decision-making and theoretical testing in science. - **Conclusion on Bayes Rule**: The speaker concludes that with the right information and a healthy skepticism, predictions can be refined over time, reinforcing the necessity for continual updating of beliefs based on new data. In summary, the talk underscores the simplicity and profound impact of Bayes' theorem on programming practices and real-world problem-solving across various domains, suggesting that Bayes is indeed 'BAE' (Before Anyone Else) in the realm of probability theory and predictive modeling.
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