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Books > Science & Mathematics > Mathematics > General
Exam Board: MEI Level: A-level Subject: Mathematics First Teaching:
September 2017 First Exam: June 2018 An OCR endorsed textbook Help
students to develop their knowledge and apply their reasoning to
mathematical problems with textbooks that draw on the well-known
MEI (Mathematics in Education and Industry) series, updated and
tailored to the 2017 OCR (MEI) specification and developed by
subject experts and MEI. - Ensure targeted development of reasoning
and problem-solving skills with plenty of practice questions and
structured exercises that build mathematical skills and techniques.
- Build connections between topics, using real-world contexts to
help develop mathematical modelling skills, thus providing a fuller
and more coherent understanding of mathematical concepts. - Address
the new statistics requirements with five dedicated statistics
chapters and questions around the use of large data sets. - Help
students to overcome misconceptions and develop insight into
problem solving with annotated worked examples. - Develop
understanding and measure progress with graduated exercises that
support students at every stage of their learning. - Provide clear
paths of progression that combine pure and applied maths into a
coherent whole.
Exploring Monte Carlo Methods is a basic text that describes the
numerical methods that have come to be known as "Monte Carlo." The
book treats the subject generically through the first eight
chapters and, thus, should be of use to anyone who wants to learn
to use Monte Carlo. The next two chapters focus on applications in
nuclear engineering, which are illustrative of uses in other
fields. Five appendices are included, which provide useful
information on probability distributions, general-purpose Monte
Carlo codes for radiation transport, and other matters. The famous
"Buffon s needle problem" provides a unifying theme as it is
repeatedly used to illustrate many features of Monte Carlo
methods.
This book provides the basic detail necessary to learn how to
apply Monte Carlo methods and thus should be useful as a text book
for undergraduate or graduate courses in numerical methods. It is
written so that interested readers with only an understanding of
calculus and differential equations can learn Monte Carlo on their
own. Coverage of topics such as variance reduction, pseudo-random
number generation, Markov chain Monte Carlo, inverse Monte Carlo,
and linear operator equations will make the book useful even to
experienced Monte Carlo practitioners.
Provides a concise treatment of generic Monte Carlo methods
Proofs for each chapter
Appendixes include Certain mathematical functions; Bose Einstein
functions, Fermi Dirac functions, Watson functions"
"Different books, different results." This book is different from
the lengthy review books. It is designed to help students review
all the important math topics when they have only six to eight
weeks before the Regents exam. This book uses real Regents
questions and shows all necessary steps to solve the problems. Its
clear format is like no other.
Is the invention of accounting so useful that, as Charlie Munger
once said, "you have to know accounting. It's the language of
practical business life. It was a very useful thing to deliver to
civilization. I've heard it came to civilization through Venice
which of course was once the great commercial power in the
Mediterranean"? (WOO 2013) This positive view on accounting can be
contrasted with an opposing view by Paul Browne that "the recent
[accounting] scandals have brought a new level of attention to the
accounting profession as gatekeepers and custodians of social
interest." (DUM 2013) Contrary to these opposing views (and other
ones as will be discussed in the book), accounting (in relation to
addition and subtraction) are neither possible (or impossible) nor
desirable (or undesirable) to the extent that the respective
ideologues (on different sides) would like us to believe. Of
course, this reexamination of different opposing views on
accounting does not mean that the study of addition and subtraction
is useless, or that those fields (related to accounting)-like
bookkeeping, auditing, forensics, info management, finance,
philosophy of accounting, accounting ethics, lean accounting,
mental accounting, environmental audit, creative accounting, carbon
accounting, social accounting, and so on-are unimportant. (WK 2013)
In fact, neither of these extreme views is plausible. Rather, this
book offers an alternative (better) way to understand the future of
accounting in regard to the dialectic relationship between addition
and subtraction-while learning from different approaches in the
literature but without favoring any one of them (nor integrating
them, since they are not necessarily compatible with each other).
More specifically, this book offers a new theory (that is, the
double-sided theory of accounting) to go beyond the existing
approaches in a novel way and is organized in four chapters. This
seminal project will fundamentally change the way that we think
about accounting in relation to addition and subtraction from the
combined perspectives of the mind, nature, society, and culture,
with enormous implications for the human future and what I
originally called its "post-human" fate.
This volume is an outgrowth of the Conference on Research on the
Enacted Mathematics Curriculum, funded by the National Science
Foundation and held in Tampa, Florida in November 2010. The volume
has the potential to be useful to a range of researchers, from
established veterans in curriculum research to new researchers in
this area of mathematics education. The chapters can be used to
generate conversation about researching the enacted mathematics
curriculum, including similarities and differences in the variables
that can and should be studied across various curricula. As such,
it might be used by a curriculum project team as it outlines a
research agenda for curriculum or program evaluation. It might also
be used as a text in a university graduate course on curriculum
research and design. The chapters in this volume are a natural
complement to those in Approaches to Studying the Enacted
Mathematics Curriculum (Heck, Chval, Weiss, & Ziebarth, 2012),
also published by Information Age Publishing. While the present
volume focuses on a range of issues related to researching the
enacted mathematics curriculum, including theoretical and
conceptual issues, the volume by Heck et al. provides insights into
different instrumentations used by groups of researchers to study
curriculum enactment.
This book is devoted to the study of stochastic measures (SMs). An
SM is a sigma-additive in probability random function, defined on a
sigma-algebra of sets. SMs can be generated by the increments of
random processes from many important classes such as
square-integrable martingales and fractional Brownian motion, as
well as alpha-stable processes. SMs include many well-known
stochastic integrators as partial cases.General Stochastic Measures
provides a comprehensive theoretical overview of SMs, including the
basic properties of the integrals of real functions with respect to
SMs. A number of results concerning the Besov regularity of SMs are
presented, along with equations driven by SMs, types of solution
approximation and the averaging principle. Integrals in the Hilbert
space and symmetric integrals of random functions are also
addressed.The results from this book are applicable to a wide range
of stochastic processes, making it a useful reference text for
researchers and postgraduate or postdoctoral students who
specialize in stochastic analysis.
Stand out, showcase your ability and succeed in your university
admissions test. Whether you're taking STEP, MAT or TMUA, this
essential guide reveals tried-and-tested strategies for building
the problem-solving skills you need to secure a high score.
Containing expert advice and worked examples, followed by
multiple-choice and extended questions that replicate the exams,
this guide is designed to improve your understanding of the
admissions tests and help to build the skills universities are
looking for. - Learn to think like a university student - detailed
guidance, thought-provoking questions and worked solutions show you
how to advance your mathematical thinking - Improve your
mathematical reasoning - practise the problem-solving skills you
need with 'Try it out' activities throughout the book and
end-of-chapter exercises to track progress - Build a path through
every problem - our authors guide you through each type of problem
so that you can approach questions confidently, think on the spot
and apply your knowledge to new contexts - Maximise marks and make
the most of the time you have - at the end of each chapter, our
authors give advice on how to tackle questions in the most
time-efficient way and help you to figure out which ones will show
off your ability What are the STEP (Sixth Term Examination Paper),
MAT (Mathematics Admissions Test) and TMUA (Test of Mathematics for
University Admission) admissions tests? These admissions tests are
used by universities as part of the application process to test
problem-solving skills and identify candidates with the highest
ability, motivation and ingenuity. MEI (Mathematics in Education
and Industry) endorses this book and provided two of the authors.
MEI is a charity and works to improve maths education, offering a
range of support for teachers, including expertly written
resources. OUR AUTHORS David Bedford has a PhD in Combinatorics and
has been a mathematics lecturer in UK universities for over 30
years. He is also an A level examiner and has extensive experience
in preparing students for mathematics admissions tests. David is
the author of the Hodder 'MEI Further Mathematics: Extra Pure
Maths' textbook. Phil Chaffe is the Advanced Maths Support
Programme 16-19 Student Support and Problem Solving Professional
Development Lead. He is the creator and lead writer for the Problem
Solving Matters course which is designed to prepare students for
mathematics admissions tests and is run in partnership with the
Universities of Oxford, Warwick, Durham, Manchester, Bristol and
Imperial College London. He is also the course designer for
Imperial College's A* in A Level Mathematics course. He is also the
MEI University Sector Lead. Tim Honeywill has been teaching at King
Henry VIII School, Coventry, since 2008. Before that, he was the
Coventry and Warwickshire Centre Manager for the Further
Mathematics Network (now the AMSP), based at the University of
Warwick where he did his PhD. He leads a ten-week Problem Solving
course for Year 12 students and is a presenter on both the Problem
Solving Matters course and on a STEP support course for Year 13
students. Richard Lissaman has a PhD in Ring Theory, a branch of
abstract algebra. He has over 10 years' experience as a mathematics
lecturer in UK universities and 20 years' experience of supporting
students with A level Mathematics, Further Mathematics and
mathematics admissions tests.
Rank-Based Methods for Shrinkage and Selection A practical and
hands-on guide to the theory and methodology of statistical
estimation based on rank Robust statistics is an important field in
contemporary mathematics and applied statistical methods.
Rank-Based Methods for Shrinkage and Selection: With Application to
Machine Learning describes techniques to produce higher quality
data analysis in shrinkage and subset selection to obtain
parsimonious models with outlier-free prediction. This book is
intended for statisticians, economists, biostatisticians, data
scientists and graduate students. Rank-Based Methods for Shrinkage
and Selection elaborates on rank-based theory and application in
machine learning to robustify the least squares methodology. It
also includes: Development of rank theory and application of
shrinkage and selection Methodology for robust data science using
penalized rank estimators Theory and methods of penalized rank
dispersion for ridge, LASSO and Enet Topics include Liu regression,
high-dimension, and AR(p) Novel rank-based logistic regression and
neural networks Problem sets include R code to demonstrate its use
in machine learning
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