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Applied Probability (ArsDigita University). Instructors: Dr. Tina Kapur and Dr. Rajeev Surati.
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Description
This course focuses on modeling, quantification, and analysis of uncertainty by teaching random variables, simple random processes and their probability distributions, Markov processes, limit theorems, elements of statistical inference, and decision making under uncertainty. This course extends the discrete probability learned in the discrete math class. It focuses on actual applications, and places little emphasis on proofs. A problem set based on identifying tumors using MRI (Magnetic Resonance Imaging) is done using Matlab. (from ADUni.org)
Course content
- Lecture 01 – Introduction, Algebra of Events, Conditional Probability Unlimited
- Lecture 02 – Independence, Bayes Theorem, Probability Mass Functions Unlimited
- Lecture 03 – Conditional PMFs, Probability Density Functions Unlimited
- Lecture 04 – PDFs and Imaged Guided Surgery Unlimited
- Lecture 05 – Bayesian Segmentation of MRI Images Unlimited
Instructor
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