Bayes theorem
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Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data. A basic…
"If you can't explain it simply, you don't understand it well enough." That's the basis of the Feynman Technique, a four-step process which can help you learn anything quickly. Here's how to use it.
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This article is about frequently asked machine learning interview questions and answers. It helps you to prepare yourself for your upcoming interview.
This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.
![This post will introduce you to bayesian regression in R, see the reference list at the end of the post for further information concerning this very broad topic. Bayesian regression Bayesian statistics turn around the Bayes theorem, which in a regression context is the following: $$ P(theta|Data) propto P(Data|theta) times P(theta) $$ Where (theta) is […]](https://i.pinimg.com/474x/7f/de/5f/7fde5ffc2d67fff53e234a1325ff04be--bayes-theorem-programming-languages.jpg)
This post will introduce you to bayesian regression in R, see the reference list at the end of the post for further information concerning this very broad topic. Bayesian regression Bayesian statistics turn around the Bayes theorem, which in a regression context is the following: $$ P(\theta|Data) \propto P(Data|\theta) \times P(\theta) $$ Where \(\theta\) is […]
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