Modelling Under Risk and Uncertainty / Wiley Series in Probability and Statistics (ePub)

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 ?

Written by a leader at the crossroads of industry, academia and
engineering, and based on decades of multi-disciplinary field
experience, Modelling Under Risk and Uncertainty gives a
self-consistent introduction to the methods involved by any type of
modeling development acknowledging the inevitable uncertainty and
associated risks. It goes beyond the "black-box" view
that some analysts, modelers, risk experts or statisticians develop
on the underlying phenomenology of the environmental or industrial
processes, without valuing enough their physical properties and
inner modelling potential nor challenging the practical
plausibility of mathematical hypotheses; conversely it is also to
attract environmental or engineering modellers to better handle
model confidence issues through finer statistical and risk analysis
material taking advantage of advanced scientific computing, to face
new regulations departing from deterministic design or support
robust decision-making.

Modelling Under Risk and Uncertainty:

* Addresses a concern of growing interest for large industries,
environmentalists or analysts: robust modeling for decision-making
in complex systems.

* Gives new insights into the peculiar mathematical and
computational challenges generated by recent industrial safety or
environmental control analysis for rare events.

* Implements decision theory choices differentiating or
aggregating the dimensions of risk/aleatory and epistemic
uncertainty through a consistent multi-disciplinary set of
statistical estimation, physical modelling, robust computation and
risk analysis.

* Provides an original review of the advanced inverse
probabilistic approaches for model identification, calibration or
data assimilation, key to digest fast-growing multi-physical data
acquisition.

* Illustrated with one favourite pedagogical example crossing
natural risk, engineering and economics, developed throughout the
book to facilitate the reading and understanding.

* Supports Master/PhD-level course as well as advanced tutorials
for professional training

Analysts and researchers in numerical modeling, applied
statistics, scientific computing, reliability, advanced
engineering, natural risk or environmental science will benefit
from this book.