The book is a review of some basics notions in optics. The first chapter starts with a review of Newton's laws and planetary motion and some related equations. The second chapter deals with the planet earth's atmosphere; the third is an introduction to remote sensing. Chapter 4 and 5 introduce a background on Maxwell's laws in electromagnetism and light polarization. Some other topics of interest have been also developed. Among these topics are the light interaction with spherical surfaces and related equations, light Interference, linear polarization by anisotropy, Fourier transform spectroscopy, and an introduction to Lidar.

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.

"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.

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"

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

The valuation of the liability structure can be determined by real options because the shares of a company can be regarded as similar to the purchase of a financial call option. Therefore, from this perspective, debt can be viewed as the sale of a financial put option. As a result, financial analysts are able to establish different valuations of a company, according to these two financing methods. Valuation of the Liability Structure by Real Options explains how the real options method works in conjunction with traditional methods. This innovative approach is particularly suited to the valuation of companies in industries where an underlying asset has high volatility (such as the mining or oil industries) or where research and development costs are high (for example, the pharmaceutical industry). Integration of the economic value of net debt (rather than the accounting value) and integration of the asset volatility are the main advantages of this approach.