Normal view MARC view ISBD view

Modelling under risk and uncertainty: an introduction to statistical, phenomenological and computational methods

By: De Rocquigny, Etienne.
Material type: materialTypeLabelBookSeries: Wiley series in probability and statistics. Publisher: West Sussex John Wiley & Sons 2012Description: xxxv, 434 p.ISBN: 9780470695142.Subject(s): Industrial management - Mathematical models | Risk management - Mathematical models | Uncertainty - Mathematical modelsDDC classification: 338.5015195 Summary: Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated: How uncertain is my model ? Is it truly valuable to support decision-making ? What kind of decision can be truly supported and how can I handle residual uncertainty ? How much refined should the mathematical description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational budget ? Should it be reduced now or later ? How robust is the analysis or the computational methods involved ? Should / could those methods be more robust ? Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether ? How reasonable is the choice of probabilistic modeling for rare events ? How rare are the events to be considered ? How far does it make sense to handle extreme events and elaborate confidence figures ? Can I take advantage of expert / phenomenological knowledge to tighten the probabilistic figures ? Are there connex domains that could provide models or inspiration for my problem ? (http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470695145.html)
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)

Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated: How uncertain is my model ? Is it truly valuable to support decision-making ? What kind of decision can be truly supported and how can I handle residual uncertainty ? How much refined should the mathematical description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational budget ? Should it be reduced now or later ? How robust is the analysis or the computational methods involved ? Should / could those methods be more robust ? Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether ? How reasonable is the choice of probabilistic modeling for rare events ? How rare are the events to be considered ? How far does it make sense to handle extreme events and elaborate confidence figures ? Can I take advantage of expert / phenomenological knowledge to tighten the probabilistic figures ? Are there connex domains that could provide models or inspiration for my problem ? (http://as.wiley.com/WileyCDA/WileyTitle/productCd-0470695145.html)

There are no comments for this item.

Log in to your account to post a comment.

Powered by Koha