(Wiley Series in Probability and Statistics Book 980) 1st Edition
by Etienne de Rocquigny (Author)
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.
