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Graeme Baker

Statistics · Columbia University

Courses Graeme Baker teaches

What students said

STAT GR5265 · Spring 2025

I would not recommend because I think the methods are not very practical (although more practically than first semester stochastic required for MAFN). 1 Columbia University: Arts & Sciences Spring 2025 Course: STATGR5265_001_2025_1-STOCHASTICMETHODSINFINANCE : STATGR5265_001_2025_1 - STOCHASTIC METHODS IN FINANCE Instructor: Graeme Baker

STAT GR5264 · Fall 2024

I learned a lot about stochastic process, especially how to derive the BS model.

STAT GU4264 · Fall 2024

A comprehensive idea of what stochastic processes are and why measure theory is important.

STAT GR9302 · Spring 2024

I was able to see and learn about the many different ways probability is being applied in the research sphere. I think it is helpful in shaping my idea of what research can look like.

STAT GR5265 · Spring 2024

Absolutely a great course to take. Well-structured contents and reasonable workload. Exams are well designed to reflect lecture contents and homework. You will be fine if you put some effort. Professor Baker is caring about students. He responses to every question patiently, tries to put everything into easy-to-understand term, and uploads notes for reference. 1 Columbia University: Arts & Sciences Spring 2024 Course: STATGR5265_001_2024_1-STOCHASTICMETHODSINFINANCE : STATGR5265_001_2024_1 - STOCHASTIC METHODS IN FINANCE Instructor: Graeme Baker

STAT GR5264 · Fall 2024

Stochastic calculus and basic measure theory.

STAT GU4264 · Fall 2024

option pricing, Brownian motion, stochastic differential equation and a bunch of lemmas and theorems

STAT GR5264 · Fall 2024

Brownian motion, Ito formula, financial derivaives, etc...

STAT GU4264 · Fall 2024

Basics of stochastic calculus, brownian motion

STAT GR5264 · Fall 2024

Best class ever! Amazing teaching methods, wonderful grading, outstanding academic atmosphere. Professor Baker's methods and teaching skills are great, and he is the greatest in explaining the stochastic processes.

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