Inverse Problem Theory and Methods for Model Parameter Estimation
by Albert Tarantola
Publisher: SIAM 2004
Number of pages: 358
The first part of the book deals exclusively with discrete inverse problems with a finite number of parameters, while the second part of the book deals with general inverse problems. The book is directed to all scientists, including applied mathematicians, facing the problem of quantitative interpretation of experimental data in fields such as physics, chemistry, biology, image processing, and information sciences. Considerable effort has been made so that this book can serve either as a reference manual for researchers or as a textbook in a course for undergraduate or graduate students.
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by David Aldous, James Allen Fill - University of California, Berkeley
From the table of contents: General Markov Chains; Reversible Markov Chains; Hitting and Convergence Time, and Flow Rate, Parameters for Reversible Markov Chains; Special Graphs and Trees; Cover Times; Symmetric Graphs and Chains; etc.
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 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 G. D'Agostini - arXiv
Triggered by a recent interesting article on the too frequent incorrect use of probabilistic evidence in courts, the author introduces the basic concepts of probabilistic inference with a toy model, and discusses several important issues.