Bayes' Theorem clearly explained:
Bayes' Theorem is a cornerstone of probability theory!
It calculates the probability of an event, given that another event has occurred.
It's like updating your guess with fresh information!
Before we delve into the details, let's take a quick look at its formula:
Imagine you're trying to guess if it will rain today. ☔️ You start with a general belief based on the weather forecast (say, a 40% chance of rain).
This is your 'prior' probability:
Then, you notice the sky is getting cloudy. ☁️
This new information is 'evidence' that might affect the probability of rain.
Next we consider P(Cloud | Rain), which is used to weigh the likelihood that it rains when it's cloudy.
Check this out👇
We are now ready to update our belief by calculating the probability of rain given it's cloudy using Bayes' Theorem!
Check it out👇
I'll leave you with a visual proof of the Bayes' Theorem!
I hope you'll enjoy it!
If you interested in:
- Python 🐍
- MLMLOps 🛠
- CV/NLP 🗣
- LLMs 🧠
- AI Engineering ⚙️
Find me → @akshay_pachaar ✔️
Everyday, I share tutorials on above topics!
Cheers! 🥂
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