Mathematical Models of Information and Stochastic Systems
Philipp Kornreich· ISBN 9781420058840
Special offer terms
Zookal Study Premium
Subscribe & save
By selecting the 'Susbcribe & Save' option you are enrolling in an auto-renewing subscription of Zookal Study Premium. Cancel at anytime.
Auto-Renewal
Your Zookal Study Premium subscription will be renewed each month until you cancel. You consent to Zookal automatically charging your payment method on file $19.99 each month after 1st month free period until you cancel.
How to Cancel
You can cancel your subscription anytime by visiting Manage account page, clicking "Manage subscription" and completing the steps to cancel. Cancellations take effect at the end of the 1st month free period (if applicable) or at the end of the current billing cycle in which your request to cancel was received. Subscription fees are not refundable.
Zookal Study Premium Monthly Subscription Includes:
Ability to post up to ten (10) questions per month.
20% off your textbooks order and free standard shipping whenever you shop online at
textbooks.zookal.com.au
Unused monthly subscription benefits have no cash value, are not transferable, and expire at the end of each month. This means that subscription benefits do not roll over to or accumulate for use in subsequent months.
Payment Methods
Afterpay and Zip Pay will not be available for purchases with Zookal Study Premium subscription added to bag.
$1.00 preauthorisation
You may see a $1.00 preauthorisation by your bank which will disappear from your statement in a few business days..
Email communications
By adding Zookal Study Premium, you agree to receive email communications from Zookal.
From ancient soothsayers and astrologists to today’s pollsters and economists, probability theory has long been used to predict the future on the basis of past and present knowledge. Mathematical Models of Information and Stochastic Systems shows that the amount of knowledge about a system plays an important role in the mathematical models used to foretell the future of the system. It explains how this known quantity of information is used to derive a system’s probabilistic properties. After an introduction, the book presents several basic principles that are employed in the remainder of the text to develop useful examples of probability theory. It examines both discrete and continuous distribution functions and random variables, followed by a chapter on the average values, correlations, and covariances of functions of variables as well as the probabilistic mathematical model of quantum mechanics. The author then explores the concepts of randomness and entropy and derives various discrete probabilities and continuous probability density functions from what is known about a particular stochastic system. The final chapters discuss information of discrete and continuous systems, time-dependent stochastic processes, data analysis, and chaotic systems and fractals. By building a range of probability distributions based on prior knowledge of the problem, this classroom-tested text illustrates how to predict the behavior of diverse systems. A solutions manual is available for qualifying instructors.