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This attractive textbook with its easy-to-follow presentation
provides a down-to-earth introduction to operations research for
students in a wide range of fields such as engineering, business
analytics, mathematics and statistics, computer science, and
econometrics. It is the result of many years of teaching and
collective feedback from students.The book covers the basic models
in both deterministic and stochastic operations research and is a
springboard to more specialized texts, either practical or
theoretical. The emphasis is on useful models and interpreting the
solutions in the context of concrete applications.The text is
divided into several parts. The first three chapters deal
exclusively with deterministic models, including linear programming
with sensitivity analysis, integer programming and heuristics, and
network analysis. The next three chapters primarily cover basic
stochastic models and techniques, including decision trees, dynamic
programming, optimal stopping, production planning, and inventory
control. The final five chapters contain more advanced material,
such as discrete-time and continuous-time Markov chains, Markov
decision processes, queueing models, and discrete-event
simulation.Each chapter contains numerous exercises, and a large
selection of exercises includes solutions.
This attractive textbook with its easy-to-follow presentation
provides a down-to-earth introduction to operations research for
students in a wide range of fields such as engineering, business
analytics, mathematics and statistics, computer science, and
econometrics. It is the result of many years of teaching and
collective feedback from students.The book covers the basic models
in both deterministic and stochastic operations research and is a
springboard to more specialized texts, either practical or
theoretical. The emphasis is on useful models and interpreting the
solutions in the context of concrete applications.The text is
divided into several parts. The first three chapters deal
exclusively with deterministic models, including linear programming
with sensitivity analysis, integer programming and heuristics, and
network analysis. The next three chapters primarily cover basic
stochastic models and techniques, including decision trees, dynamic
programming, optimal stopping, production planning, and inventory
control. The final five chapters contain more advanced material,
such as discrete-time and continuous-time Markov chains, Markov
decision processes, queueing models, and discrete-event
simulation.Each chapter contains numerous exercises, and a large
selection of exercises includes solutions.
In this undergraduate text, the author has distilled the core of
probabilistic ideas and methods for computer and data science. The
book emphasizes probabilistic and computational thinking rather
than theorems and proofs. It provides insights and motivates the
students by telling them why probability works and how to apply
it.The unique features of the book are as follows:This book
contains many worked examples. Numerous instructive problems
scattered throughout the text are given along with problem-solving
strategies. Several of the problems extend previously covered
material. Answers to all problems and worked-out solutions to
selected problems are also provided.Henk Tijms is the author of
several textbooks in the area of applied probability and stochastic
optimization. In 2008, he received the prestigious INFORMS
Expository Writing Award for his work. He also contributed engaging
probability puzzles to The New York Times' former Numberplay
column.
In this undergraduate text, the author has distilled the core of
probabilistic ideas and methods for computer and data science. The
book emphasizes probabilistic and computational thinking rather
than theorems and proofs. It provides insights and motivates the
students by telling them why probability works and how to apply
it.The unique features of the book are as follows:This book
contains many worked examples. Numerous instructive problems
scattered throughout the text are given along with problem-solving
strategies. Several of the problems extend previously covered
material. Answers to all problems and worked-out solutions to
selected problems are also provided.Henk Tijms is the author of
several textbooks in the area of applied probability and stochastic
optimization. In 2008, he received the prestigious INFORMS
Expository Writing Award for his work. He also contributed engaging
probability puzzles to The New York Times' former Numberplay
column.
The second edition represents an ongoing effort to make probability
accessible to students in a wide range of fields such as
mathematics, statistics and data science, engineering, computer
science, and business analytics. The book is written for those
learning about probability for the first time. Revised and updated,
the book is aimed specifically at statistics and data science
students who need a solid introduction to the basics of
probability.While retaining its focus on basic probability,
including Bayesian probability and the interface between
probability and computer simulation, this edition's significant
revisions are as follows:The approach followed in the book is to
develop probabilistic intuition before diving into details. The
best way to learn probability is by practising on a lot of
problems. Many instructive problems together with problem-solving
strategies are given. Answers to all problems and worked-out
solutions to selected problems are also provided.Henk Tijms is the
author of several textbooks in the area of applied probability. In
2008, he had received the prestigious INFORMS Expository Writing
Award for his work. He is active in popularizing probability at
Dutch high schools.
Probability has applications in many areas of modern science, not
to mention in our daily life. Its importance as a mathematical
discipline cannot be overrated, and it is a fascinating and
surprising topic in its own right. This engaging textbook with its
easy-to-follow writing style provides a comprehensive yet concise
introduction to the subject. It covers all of the standard material
for undergraduate and first-year-graduate-level courses as well as
many topics that are usually not found in standard texts, such as
Bayesian inference, Markov chain Monte Carlo simulation, and
Chernoff bounds.
This book brings together a variety of probability applications
through entertaining stories that will appeal to a broad
readership. What are the best stopping rules for the dating
problem? What can Bayes' formula tell us about the chances of a
Champions League draw for soccer teams being rigged? How could
syndicates win millions of lottery dollars by buying a multitude of
tickets at the right time? What's the best way to manage your
betting bankroll in a game in which you have an edge? How to use
probability to debunk quacks and psychic mediums? How can the Monte
Carlo simulation be used to solve a wide variety of probability
problems? Are seven riffle shuffles of a standard deck of 52
playing cards enough for randomness? Provides seventeen engaging
stories that illustrate ideas in probability. Written so as to be
suitable for those with minimal mathematical background. Stories
can be read independently. Can be used as examples and exercises
for teaching introductory probability. These questions and many
more are addressed in seventeen short chapters that can be read
independently. The engaging stories are instructive and demonstrate
valuable probabilistic ideas. They offer students material that
they most likely don't learn in class, and offer teachers a new way
of teaching their subject.
This book brings together a variety of probability applications
through entertaining stories that will appeal to a broad
readership. What are the best stopping rules for the dating
problem? What can Bayes' formula tell us about the chances of a
Champions League draw for soccer teams being rigged? How could
syndicates win millions of lottery dollars by buying a multitude of
tickets at the right time? What's the best way to manage your
betting bankroll in a game in which you have an edge? How to use
probability to debunk quacks and psychic mediums? How can the Monte
Carlo simulation be used to solve a wide variety of probability
problems? Are seven riffle shuffles of a standard deck of 52
playing cards enough for randomness? Provides seventeen engaging
stories that illustrate ideas in probability. Written so as to be
suitable for those with minimal mathematical background. Stories
can be read independently. Can be used as examples and exercises
for teaching introductory probability. These questions and many
more are addressed in seventeen short chapters that can be read
independently. The engaging stories are instructive and demonstrate
valuable probabilistic ideas. They offer students material that
they most likely don't learn in class, and offer teachers a new way
of teaching their subject.
'What makes this book unique among books of similar size and scope
is that when the author decided to include something in the book,
he has treated it in a way similar to the common practice in
textbooks, with very detailed and reader-friendly explanations,
fully worked-out examples, and even numerous exercises ... There
are no prerequisites beyond second-semester calculus and the book
can be used for self-study as well as in the
classroom.'CHOICEWritten by international award-winning probability
expert Henk Tijms, Basic Probability: What Every Math Student
Should Know presents the essentials of elementary probability. The
book is primarily written for high school and college students
learning about probability for the first time. In a highly
accessible way, a modern treatment of the subject is given with
emphasis on conditional probability and Bayesian probability, on
striking applications of the Poisson distribution, and on the
interface between probability and computer simulation.In modern
society, it is important to be able to critically evaluate
statements of a probabilistic nature presented in the media in
order to make informed judgments. A basic knowledge of probability
theory is indispensable to logical thinking and statistical
literacy. The book provides this knowledge and illustrates it with
numerous everyday situations.
'What makes this book unique among books of similar size and scope
is that when the author decided to include something in the book,
he has treated it in a way similar to the common practice in
textbooks, with very detailed and reader-friendly explanations,
fully worked-out examples, and even numerous exercises ... There
are no prerequisites beyond second-semester calculus and the book
can be used for self-study as well as in the
classroom.'CHOICEWritten by international award-winning probability
expert Henk Tijms, Basic Probability: What Every Math Student
Should Know presents the essentials of elementary probability. The
book is primarily written for high school and college students
learning about probability for the first time. In a highly
accessible way, a modern treatment of the subject is given with
emphasis on conditional probability and Bayesian probability, on
striking applications of the Poisson distribution, and on the
interface between probability and computer simulation.In modern
society, it is important to be able to critically evaluate
statements of a probabilistic nature presented in the media in
order to make informed judgments. A basic knowledge of probability
theory is indispensable to logical thinking and statistical
literacy. The book provides this knowledge and illustrates it with
numerous everyday situations.
The second edition represents an ongoing effort to make probability
accessible to students in a wide range of fields such as
mathematics, statistics and data science, engineering, computer
science, and business analytics. The book is written for those
learning about probability for the first time. Revised and updated,
the book is aimed specifically at statistics and data science
students who need a solid introduction to the basics of
probability.While retaining its focus on basic probability,
including Bayesian probability and the interface between
probability and computer simulation, this edition's significant
revisions are as follows:The approach followed in the book is to
develop probabilistic intuition before diving into details. The
best way to learn probability is by practising on a lot of
problems. Many instructive problems together with problem-solving
strategies are given. Answers to all problems and worked-out
solutions to selected problems are also provided.Henk Tijms is the
author of several textbooks in the area of applied probability. In
2008, he had received the prestigious INFORMS Expository Writing
Award for his work. He is active in popularizing probability at
Dutch high schools.
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