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This text is for students of engineering, science, economics and
mathematics who want to learn about Monte Carlo methods but have
only a passing acquaintance with probability theory.
The probability needed to understand the material is developed
within the text itself in a direct manner using Monte Carlo experiments
for reinforcement.
There is a prerequisite of at least one year of calculus and a
semester of matrix algebra.
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Each new idea is carefully motivated by a realistic problem, thus
leading to insights into probability theory via examples and numerical
simulations.
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Programming exercises are integrated throughout the text as the primary
vehicle for learning the material.
All examples in the text are coded in python as a representative
language; the logic is sufficiently clear so as to be easily translated
into any other language.
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Along the way, most of the basic theory of probability is developed in
order to illuminate the solutions to the questions posed.
One of the strongest features of the book is the wealth of completely
solved example problems.
These provide the reader with a sourcebook to follow towards the solution
of their own computational problems.
Each chapter ends with a large collection of homework problems illustrating
and directing the material.
Roster of algorithms in the text