This rigorous course in probability covers probability space, random variables, functions of random variables, independence and conditional probabilities, moments, joint distributions, multivariate random variables, conditional expectation and variance, distributions with random parameters, posterior distributions, probability generating function, moment generating function, characteristic function, random sum, types of convergence and relation between convergence concepts, law of large numbers and central limit theorem (i.i.d. and non- i.i.d. cases), Borel-Cantelli Lemmas, well known discrete and continuous distributions, homogenous Poisson process (HPP), non-homogenous Poisson process (NHPP), and compound Poisson process. This course is proof oriented. The primary purpose of this course is to lay the foundation for the second course, 625.722 Probability and Stochastic Process II, and other specialized courses in probability. Note that, in contrast to 625.728, this course is largely a non-measure theoretic approach to probability.
Course prerequisites: 
Multivariate calculus and 625.403 Statistical Methods and Data Analysis or equivalent
Course instructor: 
Akinpelu, Aminzadeh

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Course all programs: 
Applied and Computational Mathematics
Data Science