Convergence of Stochastic Processes
by D. Pollard
Publisher: Springer 1984
Number of pages: 223
An exposition od selected parts of empirical process theory, with related interesting facts about weak convergence, and applications to mathematical statistics. The high points of the book describe the combinatorial ideas needed to prove maximal inequalities for empirical processes indexed by classes of sets or classes of functions.
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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 J. C. Lemm - arXiv.org
A particular Bayesian field theory is defined by combining a likelihood model, providing a probabilistic description of the measurement process, and a prior model, providing the information necessary to generalize from training to non-training data.
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This book covers the history of probability up to Kolmogorov with essential additional coverage of statistics up to Fisher. The book covers an extremely wide field, and is targeted at the same readers as any other book on history of science.
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