Author : John Hebborn
Publisher : Heinemann
Page : 164 pages
File Size : 49,84 MB
Release : 2001
Category : Mathematics
ISBN : 9780435510817
A syllabus-specific textbook providing worked examples, exam-level questions and many practice exercises, in accordance to the new Edexcel AS and Advanced GCE specification.
Author : I Hardwick
Publisher : Elsevier
Page : 267 pages
File Size : 22,20 MB
Release : 1996-01-01
Category : Mathematics
ISBN : 0857099825
This text offers a complete coverage in the Decision Mathematics module, also known as Discrete Mathematics, of the syllabuses of English A-level examination boards. it is a rewritten and modern version of Decision Mathematics (published by Ellis Horwood Ltd in 1986 for The Spode Group, so well known for its development of innovative mathematics teaching). It is also a suitable text for foundation and first year undergraduate courses in qualitative studies or operational research, or for access courses for students needing strengthening in mathematics, or for students who are moving into mathematics from another subject discipline.Compact and concise, it reflects the combined teaching skills and experience of its authors who know exactly what mathematics must be learnt at the readership level today. The text is built up in modular fashion, explaining concepts used in decision mathematics and related operational research, and electronics. It emphasises an understanding of techniques and algorithms, which it relates to real life situations and working problems that will apply throughout future working careers. - Clear explanations of algorithms and all concepts - Plentiful worked examples, clear diagrams - Many exercises (with answers for self-study)
Author : John Hebborn
Publisher : Heinemann
Page : 76 pages
File Size : 15,13 MB
Release : 2002
Category : Decision making
ISBN : 9780435511319
This book covers the key topics that are tested in the Decision maths 2 exam paper.
Author : Katie Wood
Publisher : Oxford University Press - Children
Page : pages
File Size : 28,91 MB
Release : 2020-10-08
Category :
ISBN : 1382018088
This Student Book provides full support for the Further Mechanics 2 paper in the Edexcel A Level exams. The explanations throughout are clear and concise, with emphasis on visual presentation, worked examples and learning by doing. Dedicated exercises in every chapter provide practice for new exam-style problem-solving questions.
Author : Herman Chernoff
Publisher : Courier Corporation
Page : 386 pages
File Size : 28,51 MB
Release : 1986-01-01
Category : Mathematics
ISBN : 9780486652184
This well-respected introduction to statistics and statistical theory covers data processing, probability and random variables, utility and descriptive statistics, computation of Bayes strategies, models, testing hypotheses, and much more. 1959 edition.
Author : Richard Parsons
Publisher : Coordination Group Publication
Page : 204 pages
File Size : 33,7 MB
Release : 2012-05-01
Category : A-level examinations
ISBN : 9781847628060
AS/A Level Maths for Edexcel - Decision Maths 1: Student Book
Author : Jordan Ellenberg
Publisher : Penguin Press
Page : 480 pages
File Size : 21,47 MB
Release : 2014-05-29
Category : Mathematics
ISBN : 1594205221
A brilliant tour of mathematical thought and a guide to becoming a better thinker, How Not to Be Wrong shows that math is not just a long list of rules to be learned and carried out by rote. Math touches everything we do; It's what makes the world make sense. Using the mathematician's methods and hard-won insights-minus the jargon-professor and popular columnist Jordan Ellenberg guides general readers through his ideas with rigor and lively irreverence, infusing everything from election results to baseball to the existence of God and the psychology of slime molds with a heightened sense of clarity and wonder. Armed with the tools of mathematics, we can see the hidden structures beneath the messy and chaotic surface of our daily lives. How Not to Be Wrong shows us how--Publisher's description.
Author : Ronald A. Howard
Publisher : Courier Corporation
Page : 857 pages
File Size : 29,28 MB
Release : 2013-01-18
Category : Mathematics
ISBN : 0486152006
This book is an integrated work published in two volumes. The first volume treats the basic Markov process and its variants; the second, semi-Markov and decision processes. Its intent is to equip readers to formulate, analyze, and evaluate simple and advanced Markov models of systems, ranging from genetics and space engineering to marketing. More than a collection of techniques, it constitutes a guide to the consistent application of the fundamental principles of probability and linear system theory. Author Ronald A. Howard, Professor of Management Science and Engineering at Stanford University, continues his treatment from Volume I with surveys of the discrete- and continuous-time semi-Markov processes, continuous-time Markov processes, and the optimization procedure of dynamic programming. The final chapter reviews the preceding material, focusing on the decision processes with discussions of decision structure, value and policy iteration, and examples of infinite duration and transient processes. Volume II concludes with an appendix listing the properties of congruent matrix multiplication.
Author : Robert L. Childress
Publisher :
Page : 682 pages
File Size : 18,72 MB
Release : 1989
Category : Business & Economics
ISBN :
Violence prevention begins with straight talk. Studies have repeatedly shown connections between chemical dependence & every form of violent behavior, from domestic abuse to murder. Features the frank testimonials of 19 teens with significant chemical dependency issues who range in age from 13 to 22.
Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
Page : 392 pages
File Size : 45,23 MB
Release : 2020-04-23
Category : Computers
ISBN : 1108569323
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
