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Remix and Share
- Author:
-
Rajeev Surati,
Tina Kapur
- Subject:
- Science and Technology
- Institution Name:
- ArsDigita University
- Collection:
-
ArsDigita University
- Grade Level:
- Post-secondary
- Abstract:
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.
- Languages:
- English
- Material Type:
- Full Course, Homework and Assignments, Lecture Notes, Readings, Video Lectures, Other
- Media Format:
- Text/HTML, Downloadable docs, Video
- Technical Requirements:
- RealPlayer, large video files
- Conditions of Use:
-
Creative Commons Attribution-Share Alike 2.0
No restrictions on your remixing, redistributing, or making derivative works.
Give credit to the author, as required.
Your remixing, redistributing, or making derivatives works comes with some
restrictions, including how it is shared.
Your redistributing comes with some restrictions. Do not remix or make
derivative works.
Copyrighted materials, available under Fair Use and the TEACH Act for US-based
educators, or other custom arrangements. Go to the resource provider to see
their individual restrictions.
Applied Probability
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