Logo

A Minimum of Stochastics for Scientists

Small book cover: A Minimum of Stochastics for Scientists

A Minimum of Stochastics for Scientists
by

Publisher: Caltech
Number of pages: 77

Description:
The idea behind the book was to introduce students to the ideas and attitudes that underlie the statistical modeling of physical, chemical, biological systems. These pages contain material the author have tried to convey, in a course given occasionally, to a Caltech audience composed mostly of graduate students.

Home page url

Download or read it online for free here:
Download link
(730KB, PDF)

Similar books

Book cover: Reversible Markov Chains and Random Walks on GraphsReversible Markov Chains and Random Walks on Graphs
by - 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.
(14832 views)
Book cover: Inverse Problem Theory and Methods for Model Parameter EstimationInverse Problem Theory and Methods for Model Parameter Estimation
by - SIAM
The first part deals with discrete inverse problems with a finite number of parameters, while the second part deals with general inverse problems. The book for scientists and applied mathematicians facing the interpretation of experimental data.
(17478 views)
Book cover: CK-12 Basic Probability and Statistics: A Short CourseCK-12 Basic Probability and Statistics: A Short Course
by - CK-12.org
CK-12 Foundation's Basic Probability and Statistics– A Short Course is an introduction to theoretical probability and data organization. Students learn about events, conditions, random variables, and graphs and tables that allow them to manage data.
(21068 views)
Book cover: Markov Chains and Mixing TimesMarkov Chains and Mixing Times
by - American Mathematical Society
An introduction to the modern approach to the theory of Markov chains. The main goal of this approach is to determine the rate of convergence of a Markov chain to the stationary distribution as a function of the size and geometry of the state space.
(14699 views)