Randomness and Hyper-randomness : Mathematical Engineering - Igor I. Gorban

eTEXT

Randomness and Hyper-randomness

By: Igor I. Gorban

eText | 31 August 2017

At a Glance

eText


$219.00

or 4 interest-free payments of $54.75 with

 or 

Instant online reading in your Booktopia eTextbook Library *

Read online on
Desktop
Tablet
Mobile

Not downloadable to your eReader or an app

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

The monograph compares two approaches that describe the statistical stability phenomenon - one proposed by the probability theory that ignores violations of statistical stability and another proposed by the theory of hyper-random phenomena that takes these violations into account. There are five parts. The first describes the phenomenon of statistical stability. The second outlines the mathematical foundations of probability theory. The third develops methods for detecting violations of statistical stability and presents the results of experimental research on actual processes of different physical nature that demonstrate the violations of statistical stability over broad observation intervals. The fourth part outlines the mathematical foundations of the theory of hyper-random phenomena. The fifth part discusses the problem of how to provide an adequate description of the world.
The monograph should be interest to a wide readership: from university students on a first course majoring in physics, engineering, and mathematics to engineers, post-graduate students, and scientists carrying out research on the statistical laws of natural physical phenomena, developing and using statistical methods for high-precision measurement, prediction, and signal processing over broad observation intervals.
To read the book, it is sufficient to be familiar with a standard first university course on mathematics.

Read online on
Desktop
Tablet
Mobile

More in Applied Mathematics