Rutgers University 3 credits Computer Science
An introduction to probability theory and the modeling and analysis of probabilistic systems with emphasis on applications in computer science, engineering, and data science. Probabilistic models, conditional probability. Discrete and continuous random variables. Expectation and conditional expectation. Limit Theorems. Bernoulli and Poisson processes. Markov chains. Bayesian estimation and hypothesis testing. Elements of statistical inference.
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