BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Abstract Probabilistic Semantics for the Analysis of Bio Sys Models

Abstract Probabilistic Semantics for the Analysis of Bio Sys Models

Abstract Probabilistic Semantics for the Analysis of Bio Sys Models
Stock photo: cover may vary

Abstract Probabilistic Semantics for the Analysis of Bio Sys Models Papeback -

by Scatena Guido

Add to wish list
  • New
New

Description

VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG , pp. 176 . Papeback. New.
Ask the seller a question Add to wish list
A$178.93
A$5.64 Delivery within USA
Standard delivery: 9 to 14 days
More delivery options
Ships from Cold Books (New York, United States)

Details

  • Title Abstract Probabilistic Semantics for the Analysis of Bio Sys Models
  • Author Scatena Guido
  • Binding Papeback
  • Condition New
  • Pages 176
  • Volumes 1
  • Language ENG
  • Publisher VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG
  • Publication date pp. 176
  • Bookseller's Inventory # 6128812979
  • ISBN 9783659291708 / 3659291706
  • Weight 0.59 lbs (0.27 kg)
  • Dimensions 9 x 6 x 0.41 in (22.86 x 15.24 x 1.04 cm)
  • Category Computers - General Information
  • Quantity available 4

About Cold Books New York, United States

Biblio member since 2012

Terms of Sale: 30 day return guarantee, with full refund including shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Cold Books

Reader reviews for Abstract Probabilistic Semantics for the Analysis of Bio Sys Models

From the publisher

This book concerns the development of probabilistic semantics tailored to model the dynamic behavior of biological systems in order to formally analyze them. More specifically, it attempts to overcome problems, related to uncertainty and to the state space explosion, inherent to models describing biological systems. Recently, many formalisms originated from Computer Science have been successfully applied to describe biological systems. Many of these formalisms include probabilistic aspects, and techniques like stochastic simulation and probabilistic model checking have been proposed to study biological systems properties. However, the practical application of formal analysis tools in this context is still limited. The size of state space associated with models is often prohibitively large. Moreover, the knowledge of biological processes is often incomplete, resulting in models with uncertain parameters. To overcome these problems, in this Thesis, we propose to apply abstraction techniques to probabilistic semantics of biological systems models.
tracking-