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weisser-zwerg.dev | ||
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jaketae.github.io
So far on this blog, we have looked the mathematics behind distributions, most notably binomial, Poisson, and Gamma, with a little bit of exponential. These distributions are interesting in and of themselves, but their true beauty shines through when we analyze them under the light of Bayesian inference. In today's post, we first develop an intuition for conditional probabilities to derive Bayes' theorem. From there, we motivate the method of Bayesian inference as a means of understanding probability. |
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www.quantstart.com
Bayesian Statistics: A Beginner's Guide |
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www.djmannion.net
Data are sometimes on a circular scale, such as the angle of an oriented stimulus, and the analysis of such data often needs to take this circularity into account. Here, we will look at how we can use PyMC to fit a model to circular data. |
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srconstantin.github.io
Trained in math and machine learning, currently trying to solve technological problems and make sense of our world. |
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