Cholesky Decomposition and Its Applications in Python
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- Опубліковано 6 вер 2024
- In this video, we go over Cholesky decomposition of symmetric matrices. In terms of solving systems of linear equations, it is very similar to LU decomposition. I will show how to do the decomposition in and how to solve a system of linear equations using Numpy’s and Scipy’s built-in functions. Then we will explore how we can use this to generate correlated random variables. This has applications in finance where one might wish to do a Monte Carlo simulation on two or more correlated stocks.
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Hi I am a ph.d. student in South Korea. I learned a lot from this video. Thanks
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Congtatulations for the content! I hope you'll answer me :D
Rather than writing out a long reply, I'll answer in a short vidoe. I expect to have it out my the middle of this week.
@@kpmooney I have a very similar question, a video reply would be really helpful. Thanks for the content ❤️
@@kpmooney Points you can cover in video would be:
1. Your education and career in Quantitative Finance
2. What would you do differently
3. Advice for graduates and undergraduates to work in QF
4. Careers in QF
5. Master's/Certifications to pursue
6. Best paid/free resources for self-learning
7. Future of QF (Buzzwords vs Reality)
Are there any Kalman filter vids in the making?
I thought about it but haven't planned anything out yet. It'll be a while before I can get to it.
Why using the Cholesky decomposition while we can use directly the "np.linalg.solve" comand?
Speed. For solving many equations where the matrix doesn't change, but only the b vector in Ax = b changes, this would be much faster.
@@kpmooney Ah ok. Thx for explanation.