Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
Bayesian-Statistics-Econometrics
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MIT License | MIT License |
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Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
- Probabilistic Programming and Bayesian Methods for Hackers (2013)
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[Q] Bayesian statistics!
Also this is quite nice practical introduction which might help with finding answers to your questions: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
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How many of you have used algebra, calculus, geometry, etc in your business careers/the real world?
This is a good intro to probabilistic programming.
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Suggestions for some best books on computer vision
Probabilistic programming is a nice technique to have up your sleeve.
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Bayes examples and study help
+1 for Statistical Rethinking. Iβm also partial to Bayesian Methods for Hackers.
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β¨ 10 Free Books for Machine Learning & Data Science π
π https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
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Predicting the distribution of a variable rather than a point estimate
Youβre welcome! I would recommend Bayesian Methods for Hackers
- Bayesian Methods for Hackers
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A collaborative book on DeFi
All content is open-source: everyone is free to read, but also to contribute to the book using github. I know of one other book that followed this open-source 'publishing' model and became quite successful eventually through community efforts. I contemplated for a bit to create a book DAO but I think it's going to be overkill :).
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[R] Analysis of Russian vaccine trial outcomes suggests they are lazily faked. Distribution of efficacies across age groups is quite improbable
Jake Vanderplas's Statistics for Hackers presentation is a perfect place to start. Bayesian Methods for Hackers is also very good.
Bayesian-Statistics-Econometrics
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Python for Econometrics for Practitioners [Free Online Courses]
Bayesian Statistics with Python: Bayesian statistics is the last pillar of quantitative framework, also the most challenging subject. The course will explore the algorithms of Markov chain Monte Carlo (MCMC), specifically Metropolis-Hastings, Gibbs Sampler and etc., we will build up our own toy model from crude Python functions. In the meanwhile, we will cover the PyMC3, which is a library for probabilistic programming specializing in Bayesian statistics.
What are some alternatives?
skbel - SKBEL - Bayesian Evidential Learning framework built on top of scikit-learn.
edward2 - A simple probabilistic programming language.
DataScienceProjects - The code repository for projects and tutorials in R and Python that covers a variety of topics in data visualization, statistics sports analytics and general application of probability theory.
Econometrics-With-Python - Tutorials of econometrics featuring Python programming. This is a crash course for reviewing the most important concepts and techniques of basic econometrics, the theories are presented lightly without hustles of derivation and Python codes are straightforward.
machine-learning-cheat-sheet - Classical equations and diagrams in machine learning
Basic-Statistics-With-Python - Introduction to statistics featuring Python. This series of lecture notes aim to walk you through all basic concepts of statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand.