This is a course on the fundamentals of probability geared towards first- or second-year graduate students who are interested in a rigorous development of the subject. The course covers most of the topics in 6.431 (sample space, random variables, expectations, transforms, Bernoulli and Poisson processes, finite Markov chains, limit theorems) but at a faster pace and in more depth. There are also a number of additional topics, such as language, terminology, and key results from measure theory; interchange of limits and expectations; multivariate Gaussian distributions; deeper understanding of conditional distributions and expectations.
This is a graduate level class on probability theory, geared towards students who are interested in a rigorous development of the subject.
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e-book enables you to access interactive reading material to strengthen the skills learned in the sessions.
Online Lab enables you to practice the application of concepts you have learnt in the sessions in the virtual environment.
Tutorials enables you to get easy learning with clear, crisp, and to-the-point content on a wide range of technical and non-technical subjects without any preconditions and impediments.