Probability, Statistical Optics, and Data Testing: A Problem Solving Approach

Couverture
Springer Science & Business Media, 6 déc. 2012 - 404 pages
A basic skill in probability is practically demanded nowadays in many bran ches of optics, especially in image science. On the other hand, there is no text presently available that develops probability, and its companion fields stochastic processes and statistics, from the optical perspective. [Short of a book, a chapter was recently written for this purpose; see B. R. Frieden (ed. ): The Computer in Optical Research, Topics in Applied Physics, Vol. 41 (Springer, Berlin, Heidelberg, New York 1980) Chap. 3] Most standard texts either use illustrative examples and problems from electrical engineering or from the life sciences. The present book is meant to remedy this situation, by teaching probability with the specific needs of the optical researcher in mind. Virtually all the illustrative examples and applications of the theory are from image science and other fields of optics. One might say that photons have replaced electrons in nearly all considera tions here. We hope, in this manner, to make the learning of probability a pleasant and absorbing experience for optical workers. Some of the remaining applications are from information theory, a con cept which complements image science in particular. As will be seen, there are numerous tie-ins between the two concepts. Students will be adequately prepared for the material in this book if they have had a course in calculus, and know the basics of matrix manipulation.
 

Table des matières

Introduction
1
3
2
The Axiomatic Approach
7
2
13
2
19
2
25
9
33
Probability Density Function Basic Properties
37
Stochastic Processes
177
7
188
8
197
6
203
12
206
Estimating the Mean
233
Estimating a Probability Law
264
The ChiSquare Test of Significance
294

10
45
9
52
Fourier Methods in Probability
70
16
79
23
85
29
93
Functions of Random Variables
99
7
105
Physical Layout
106
10
112
Bernoulli Trials and its Limiting Cases
134
5
140
8
146
Producing Random Numbers that Obey a Prescribed
163
The Student tTest on the Mean
307
6
314
The FTest on Variance
320
1
327
4
333
Principal Components Analysis
350
The Controversy Between Bayesians and Classicists 363
362
Appendix A Error Function and its Derivative 4 12
374
223
378
Appendix E A Crib Sheet of Statistical Parameters and their Errors
382
Subject Index
393
350
394
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