**Inverse Problem Theory and Methods for Model Parameter Estimation**

by Albert Tarantola

**Publisher**: SIAM 2004**ISBN/ASIN**: 0898715725**ISBN-13**: 9780898715729**Number of pages**: 358

**Description**:

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.

Download or read it online for free here:

**Download link**

(20MB, PDF)

## Similar books

**Bayesian Field Theory**

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.

(

**1393**views)

**A Minimum of Stochastics for Scientists**

by

**Noel Corngold**-

**Caltech**

The book introduces students to the ideas and attitudes that underlie the statistical modeling of physical, chemical, biological systems. The text contains material the author have tried to convey to an audience composed mostly of graduate students.

(

**7887**views)

**Introduction to Randomness and Statistics**

by

**Alexander K. Hartmann**-

**arXiv**

This is 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.

(

**9078**views)

**Correlation and Causality**

by

**David A. Kenny**-

**John Wiley & Sons Inc**

This text is a general introduction to the topic of structural analysis. It presumes no previous acquaintance with causal analysis. It is general because it covers all the standard, as well as a few nonstandard, statistical procedures.

(

**11169**views)