Your cart is empty
- Search By ISBN, Title or Subject
- Search By ISBN, Title or Subject
- Search by Institute & Course Code
The Primer
Complex mathematical and computational models are used in all areasMathematical models are good at mapping assumptions into
inferences. A modeller makes assumptions about laws pertaining to
the system, about its status and a plethora of other, often arcane,
system variables and internal model settings. To what extent can we
rely on the model-based inference when most of these assumptions
are fraught with uncertainties? Global Sensitivity Analysis offers
an accessible treatment of such problems via quantitative
sensitivity analysis, beginning with the first principles and
guiding the reader through the full range of recommended practices
with a rich set of solved exercises. The text explains the
motivation for sensitivity analysis, reviews the required
statistical concepts, and provides a guide to potential
applications.
The book:
Postgraduate students and practitioners in a wide range of
subjects, including statistics, mathematics, engineering, physics,
chemistry, environmental sciences, biology, toxicology, actuarial
sciences, and econometrics will find much of use here. This book
will prove equally valuable to engineers working on risk analysis
and to financial analysts concerned with pricing and hedging.
The Primer
Complex mathematical and computational models are used in all areasMathematical models are good at mapping assumptions into
inferences. A modeller makes assumptions about laws pertaining to
the system, about its status and a plethora of other, often arcane,
system variables and internal model settings. To what extent can we
rely on the model-based inference when most of these assumptions
are fraught with uncertainties? Global Sensitivity Analysis offers
an accessible treatment of such problems via quantitative
sensitivity analysis, beginning with the first principles and
guiding the reader through the full range of recommended practices
with a rich set of solved exercises. The text explains the
motivation for sensitivity analysis, reviews the required
statistical concepts, and provides a guide to potential
applications.
The book:
Postgraduate students and practitioners in a wide range of
subjects, including statistics, mathematics, engineering, physics,
chemistry, environmental sciences, biology, toxicology, actuarial
sciences, and econometrics will find much of use here. This book
will prove equally valuable to engineers working on risk analysis
and to financial analysts concerned with pricing and hedging.