An Introduction to Stochastic PDEs
by Martin Hairer
Publisher: arXiv 2009
Number of pages: 78
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.
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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 O. Melchert - arXiv
In these lecture notes, a selection of frequently required statistical tools will be introduced and illustrated. They allow to post-process data that stem from, e.g., large-scale numerical simulations (aka sequence of random experiments).
by R. A. Bailey - Cambridge University Press
This book develops a coherent framework for thinking about factors that affect experiments and their relationships, including the use of Hasse diagrams. The book is ideal for advanced undergraduate and beginning graduate courses.
This book is developed as a free, collaborative and interactive learning environment for elementary probability and statistics education. The book blends information technology, scientific techniques and modern pedagogical concepts.