Union, Intersection, Independence, Disjoint, Complement: Advanced Probability for Data Science Series (1)
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If you’ve been following my previous articles in the probability series, you may have noticed that I briefly touched on concepts like probability notations before diving into Bayes’ theorem.
I took some time to look back at my articles and realized that I didn’t go deeply into the foundational notations that set the basis for all probability calculations such as the Union, Intersection, Independence, Disjoint, etc.
These notations aren’t just something that should be brushed over because they are super important in all things related to data. Especially in fields like data analysis, machine learning, and statistical modeling.
This realization led me to think: before jumping headfirst into advanced topics like Conditional Probability, Conditional Independence, Bayes’ Theorem, Markov Chains, or Monte Carlo methods, it’s crucial to have a solid understanding of the basics.
Without this foundation, advanced probability…