Introduction to Randomness and Statistics
by Alexander K. Hartmann
Publisher: arXiv 2009
Number of pages: 95
This text provides a practical introduction to randomness and data analysis, in particular in the context of computer simulations. At the beginning, the most basics concepts of probability are given, in particular discrete and continuous random variables. The text is basically self-contained, comes with several example C programs and contains eight practical exercises.
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by D. Pollard - Springer
Selected parts of empirical process theory, with applications to mathematical statistics. The book describes the combinatorial ideas needed to prove maximal inequalities for empirical processes indexed by classes of sets or classes of functions.
by Cosma Rohilla Shalizi - Cambridge University Press
This is a draft textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes.
by Luc Devroye - Springer
The book on small field on the crossroads of statistics, operations research and computer science. The applications of random number generators are wide and varied. The study of non-uniform random variates is precisely the subject area of the book.
by Martin Hairer - arXiv
This text is an attempt to give a reasonably self-contained presentation of the basic theory of stochastic partial differential equations, taking for granted basic measure theory, functional analysis and probability theory, but nothing else.