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Just pick a random place to start, read some stuff, and then take a guess as to which direction to go in next, based on what's probably a good next thing to read. Then keep repeating the process over and over again.
Are you describing Markov chains or how to learn about Markov chains?
If you have time to read step-by-step derivations and want to understand the fundamentals, I think this is an excellent self-contained resource.
Just pick a random place to start, read some stuff, and then take a guess as to which direction to go in next, based on what's probably a good next thing to read. Then keep repeating the process over and over again.
Are you describing Markov chains or how to learn about Markov chains?
As others have said, if you know know probability, start there.
Just pick a random place to start, read some stuff, and then take a guess as to which direction to go in next, based on what's probably a good next thing to read. Then keep repeating the process over and over again.
It's also important that you base your guess of what's probably good to read next only on the previous thing you read. Forget everything that came before that.
2. Poisson processes.
3. The Markov property.
4. Stochastic processes.
5. Realise that you’re missing a background in analysis, therefore you don’t know sh?t about measure theory but you actually need it to know anything deeper . Wonder to yourself if you really want to spend the next 3 years getting a maths background you don’t have.
6. Convince yourself that it’s all just engineering and middle through by picking a project involving non trivial markov chain.
7. Go back and spend 3 years doing foundational maths then repeat point 1-5.
1. Elementary probability theory. 2. Poisson processes. 3. The Markov property. 4. Stochastic processes. 5. Realise that you’re missing a background in analysis, therefore you don’t know sh?t about measure theory but you actually need it to know anything deeper . Wonder to yourself if you really want to spend the next 3 years getting a maths background you don’t have. 6. Convince yourself that it’s all just engineeri…
These are my favorite lecture notes, they assume almost no a-priori knowledge (with an awesome review of basic probabilities) and yet they don't shy away from explaining all the rigorous math. If you have time to read step-by-step derivations and want to understand the fundamentals, I think this is an excellent self-contained resource. https://ermongroup.github.io/cs228-notes/
Rigorous maths is akin to trying to explain to your non technical friends what you do in devops: colloquialise it all you want, it’ll always be a shallow story.
1. Elementary probability theory. 2. Poisson processes. 3. The Markov property. 4. Stochastic processes. 5. Realise that you’re missing a background in analysis, therefore you don’t know sh?t about measure theory but you actually need it to know anything deeper . Wonder to yourself if you really want to spend the next 3 years getting a maths background you don’t have. 6. Convince yourself that it’s all just engineeri…