Introduction to Probability - The Science of Uncertainty
An introduction to probabilistic models, including random processes and the basic elements of statistical inference.
Register for Introduction to Probability from MIT at http://edx.org/courses.
About this Course
The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.
Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem - proof" format, we develop the material in an intuitive -- but still rigorous and mathematically precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.
The course covers all of the basic probability concepts, including:
- multiple discrete or continuous random variables, expectations, and conditional distributions
- laws of large numbers
- the main tools of Bayesian inference methods
- an introduction to random processes (Poisson processes and Markov chains)
The contents of this course are essentially the same as those of the corresponding MIT class (Probabilistic Systems Analysis and Applied Probability) -- a course that has been offered and continuously refined over more than 50 years. It is a challenging class, but it will enable you to apply the tools of probability theory to real-world applications or your research.
John Tsitsiklis is a Professor with the Department of Electrical Engineering and Computer Science, and a member of the National Academy of Engineering. He obtained his PhD from MIT and joined the faculty in 1984. His research focuses on the analysis and control of stochastic systems, including applications in various domains, from computer networks to finance. He has been teaching probability for over 15 years.
Patrick Jaillet is a Professor of Electrical Engineering and Computer Science and Co-Director of the MIT Operations Research Center. He obtained his PhD in Operations Research at MIT. His research interests deal with optimization and decision making under uncertainty as applied to transportation and the internet economy. Professor Jaillet's teaching includes subjects such as algorithms, optimization, and probability.
Tagged under: Uncertainty (Concepts/Theories),Probability (Measurement System),Math,Massachusetts Institute Of Technology (College/University),MITx,edX,John Tsitsiklis,Patrick Jaillet,Probability Theory (Field Of Study),Discrete Variables,Continuous Variables,Laws Large Numbers,Probabilistic Modeling,Statistical Inference,Bayesian Inference (Literature Subject)
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