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Foundations of Statistics for Data Scientists With R and Python

Foundations of Statistics for Data Scientists With R and Python

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar including probability distributions descriptive and inferential statistical methods and linear modeling. The book assumes knowledge of basic calculus so the presentation can focus on why it works as well as how to do it. Compared to traditional mathematical statistics textbooks however the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e. g. showing an example using the lasso) classification and clustering and implementing methods with modern software (R and Python). Contains nearly 500 exercises. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists such as Bayesian inference generalized linear models for non-normal responses (e. g. logistic regression and Poisson loglinear models) and regularized model fitting. The nearly 500 exercises are grouped into Data Analysis and Applications and Methods and Concepts. Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website (http://stat4ds. rwth-aachen. de/) has expanded R Python and Matlab appendices and all data sets from the examples and exercises. | Foundations of Statistics for Data Scientists With R and Python

GBP 82.99
1

Introduction to Bioinformatics with R A Practical Guide for Biologists

Introduction to Bioinformatics with R A Practical Guide for Biologists

In biological research the amount of data available to researchers has increased so much over recent years it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required readers will develop the ability to plan suitable analyses of biological datasets and to use the R programming environment to perform these analyses. This is achieved through a series of case studies using R to answer research questions using molecular biology datasets. Broadly applicable statistical methods are explained including linear and rank-based correlation distance metrics and hierarchical clustering hypothesis testing using linear regression proportional hazards regression for survival data and principal component analysis. These methods are then applied as appropriate throughout the case studies illustrating how they can be used to answer research questions. Key Features: · Provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming. · Describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook from basic principles · Presents walk-throughs of data analysis tasks using R and example datasets. All R commands are presented and explained in order to enable the reader to carry out these tasks themselves. · Uses outputs from a large range of molecular biology platforms including DNA methylation and genotyping microarrays; RNA-seq genome sequencing ChIP-seq and bisulphite sequencing; and high-throughput phenotypic screens. · Gives worked-out examples geared towards problems encountered in cancer research which can also be applied across many areas of molecular biology and medical research. This book has been developed over years of training biological scientists and clinicians to analyse the large datasets available in their cancer research projects. It is appropriate for use as a textbook or as a practical book for biological scientists looking to gain bioinformatics skills. | Introduction to Bioinformatics with R A Practical Guide for Biologists

GBP 52.99
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Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources the Bayesian approach provides a flexible framework for drug development. Despite its advantages the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners Bayesian Analysis with R for Drug Development: Concepts Algorithms and Case Studies describes a wide range of Bayesian applications to problems throughout pre-clinical clinical and Chemistry Manufacturing and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical clinical and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang Ph. D. is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books 15 book chapters and over 90 peer-reviewed papers on diverse scientific and statistical subjects including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick Ph. D. is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences having developed and taught courses in several areas including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences. | Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

GBP 38.99
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3D Animation for the Raw Beginner Using Autodesk Maya 2e

3D Animation for the Raw Beginner Using Autodesk Maya 2e

3D Animation for the Raw Beginner Using Autodesk Maya is a hands-on academic textbook as well as a do-it-yourself training manual for the individual animator. This second edition has been completely rewritten to take into account updates to Autodesk Maya including Autodesk’s renderer Arnold. It contains entirely new examples and tutorial lessons. All 612 images are in full color. The book directs the reader to the parts of Maya that must be mastered in order to create complete 3D projects and thus it simplifies the process of taking on Maya’s vast and intricate interface while giving the reader a firm foundation on which to build future knowledge of Maya. It also presents brief examples of other popular 3D applications and rendering engines. This principles-based yet pragmatic book: Introduces the basic steps of the 3D modeling materials animation lighting and rendering processes. Presents clear and concise tutorials that link key concepts to practical techniques. Includes access to a webpage for the book: https://buzzking. com/AnimationTextbook/AnimationTextbook. html. On this webpage are videos that cover many of the lessons in the book as well as video tutorials that present bonus material not included in the book. Frees instructors from the painstaking task of developing step-by-step examples to present Maya’s complex interface and basic capabilities. Boasts an easy-to-follow tutorial-based learning style ideal for individual study by aspiring animators and do-it yourselfers. Roger Buzz King is a Professor Emeritus at the University of Colorado at Boulder where he teaches 3D Animation for the Computer Science Department and the Alliance for Technology Learning and Society (ATLAS) an institute dedicated to the application of technology to the arts. Buzz is an independent 3D animator who serves on the board of directors of a 3D animation startup. Buzz has a B. A. in Mathematics from Occidental College an M. S. and Ph. D. in Computer Science from the University of Southern California and an M. Div. from the Iliff School of Theology. Key Features Introduces critical aspects of the 3D animation process Presents clear and concise tutorials that link key concepts to practical techniques Includes access to a dedicated Web site http://3dbybuzz. com featuring useful videos lessons and updates Frees instructors from developing step-by-step examples to present Maya’s complex interface and basic Boasts an easy-to-follow hands-on learning style ideal for individual study by aspiring animators and do-ityourselfers | 3D Animation for the Raw Beginner Using Autodesk Maya 2e

GBP 48.99
1

Python for Beginners

Python for Beginners

Python is an amazing programming language. It can be applied to almost any programming task. It allows for rapid development and debugging. Getting started with Python is like learning any new skill: it’s important to find a resource you connect with to guide your learning. Luckily there’s no shortage of excellent books that can help you learn both the basic concepts of programming and the specifics of programming in Python. With the abundance of resources it can be difficult to identify which book would be best for your situation. Python for Beginners is a concise single point of reference for all material on python. Provides concise need-to-know information on Python types and statements special method names built-in functions and exceptions commonly used standard library modules and other prominent Python tools Offers practical advice for each major area of development with both Python 3. x and Python 2. x Based on the latest research in cognitive science and learning theory Helps the reader learn how to write effective idiomatic Python code by leveraging its best—and possibly most neglected—features This book focuses on enthusiastic research aspirants who work on scripting languages for automating the modules and tools development of web applications handling big data complex calculations workflow creation rapid prototyping and other software development purposes. It also targets graduates postgraduates in computer science information technology academicians practitioners and research scholars.

GBP 120.00
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C++ for Financial Mathematics

C++ for Financial Mathematics

If you know a little bit about financial mathematics but don’t yet know a lot about programming then C++ for Financial Mathematics is for you. C++ is an essential skill for many jobs in quantitative finance but learning it can be a daunting prospect. This book gathers together everything you need to know to price derivatives in C++ without unnecessary complexities or technicalities. It leads the reader step-by-step from programming novice to writing a sophisticated and flexible financial mathematics library. At every step each new idea is motivated and illustrated with concrete financial examples. As employers understand there is more to programming than knowing a computer language. As well as covering the core language features of C++ this book teaches the skills needed to write truly high quality software. These include topics such as unit tests debugging design patterns and data structures. The book teaches everything you need to know to solve realistic financial problems in C++. It can be used for self-study or as a textbook for an advanced undergraduate or master’s level course.

GBP 44.99
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Introduction to Mathematical Modeling

Introduction to Mathematical Modeling

Introduction to Mathematical Modeling helps students master the processes used by scientists and engineers to model real-world problems including the challenges posed by space exploration climate change energy sustainability chaotic dynamical systems and random processes. Primarily intended for students with a working knowledge of calculus but minimal training in computer programming in a first course on modeling the more advanced topics in the book are also useful for advanced undergraduate and graduate students seeking to get to grips with the analytical numerical and visual aspects of mathematical modeling as well as the approximations and abstractions needed for the creation of a viable model.

GBP 84.99
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Python for Bioinformatics

Python for Bioinformatics

In today's data driven biology programming knowledge is essential in turning ideas into testable hypothesis. Based on the author’s extensive experience Python for Bioinformatics Second Edition helps biologists get to grips with the basics of software development. Requiring no prior knowledge of programming-related concepts the book focuses on the easy-to-use yet powerful Python computer language. This new edition is updated throughout to Python 3 and is designed not just to help scientists master the basics but to do more in less time and in a reproducible way. New developments added in this edition include NoSQL databases the Anaconda Python distribution graphical libraries like Bokeh and the use of Github for collaborative development.

GBP 74.99
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Geometry for the Artist

Geometry for the Artist

Geometry for the Artist is based on a course of the same name which started in the 1980s at Maharishi International University. It is aimed both at artists willing to dive deeper into geometry and at mathematicians open to learning about applications of mathematics in art. The book includes topics such as perspective symmetry topology fractals curves surfaces and more. A key part of the book’s approach is the analysis of art from a geometric point of view—looking at examples of how artists use each new topic. In addition exercises encourage students to experiment in their own work with the new ideas presented in each chapter. This book is an exceptional resource for students in a general-education mathematics course or teacher-education geometry course and since many assignments involve writing about art this text is ideal for a writing-intensive course. Moreover this book will be enjoyed by anyone with an interest in connections between mathematics and art. Features Abundant examples of artwork displayed in full color Suitable as a textbook for a general-education mathematics course or teacher-education geometry course Designed to be enjoyed by both artists and mathematicians

GBP 44.99
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Artificial Intelligence for Autonomous Networks

Artificial Intelligence for Autonomous Networks

Artificial Intelligence for Autonomous Networks introduces the autonomous network by juxtaposing two unique technologies and communities: Networking and AI. The book reviews the technologies behind AI and software-defined network/network function virtualization highlighting the exciting opportunities to integrate those two worlds. Outlining the new frontiers for autonomous networks this book highlights their impact and benefits to consumers and enterprise customers. It also explores the potential of the autonomous network for transforming network operation cyber security enterprise services 5G and IoT infrastructure monitoring and traffic optimization and finally customer experience and care. With contributions from leading experts this book will provide an invaluable resource for network engineers software engineers artificial intelligence and machine learning researchers.

GBP 46.99
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Metamodeling for Variable Annuities

Metamodeling for Variable Annuities

This book is devoted to the mathematical methods of metamodeling that can be used to speed up the valuation of large portfolios of variable annuities. It is suitable for advanced undergraduate students graduate students and practitioners. It is the goal of this book to describe the computational problems and present the metamodeling approaches in a way that can be accessible to advanced undergraduate students and practitioners. To that end the book will not only describe the theory of these mathematical approaches but also present the implementations. | Metamodeling for Variable Annuities

GBP 44.99
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Numerical Methods for Finance

Numerical Methods for Finance

Featuring international contributors from both industry and academia Numerical Methods for Finance explores new and relevant numerical methods for the solution of practical problems in finance. It is one of the few books entirely devoted to numerical methods as applied to the financial field. Presenting state-of-the-art methods in this area the book first discusses the coherent risk measures theory and how it applies to practical risk management. It then proposes a new method for pricing high-dimensional American options followed by a description of the negative inter-risk diversification effects between credit and market risk. After evaluating counterparty risk for interest rate payoffs the text considers strategies and issues concerning defined contribution pension plans and participating life insurance contracts. It also develops a computationally efficient swaption pricing technology extracts the underlying asset price distribution implied by option prices and proposes a hybrid GARCH model as well as a new affine point process framework. In addition the book examines performance-dependent options variance reduction Value at Risk (VaR) the differential evolution optimizer and put-call-futures parity arbitrage opportunities. Sponsored by DEPFA Bank IDA Ireland and Pioneer Investments this concise and well-illustrated book equips practitioners with the necessary information to make important financial decisions.

GBP 59.99
1

Mathematics for Engineers and Scientists

Mathematics for Engineers and Scientists

Since its original publication in 1969 Mathematics for Engineers and Scientists has built a solid foundation in mathematics for legions of undergraduate science and engineering students. It continues to do so but as the influence of computers has grown and syllabi have evolved once again the time has come for a new edition. Thoroughly revised to meet the needs of today's curricula Mathematics for Engineers and Scientists Sixth Edition covers all of the topics typically introduced to first- or second-year engineering students from number systems functions and vectors to series differential equations and numerical analysis. Among the most significant revisions to this edition are:Simplified presentation of many topics and expanded explanations that further ease the comprehension of incoming engineering studentsA new chapter on double integralsMany more exercises applications and worked examplesA new chapter introducing the MATLAB and Maple software packagesAlthough designed as a textbook with problem sets in each chapter and selected answers at the end of the book Mathematics for Engineers and Scientists Sixth Edition serves equally well as a supplemental text and for self-study. The author strongly encourages readers to make use of computer algebra software to experiment with it and to learn more about mathematical functions and the operations that it can perform.

GBP 170.00
1

Statistical Reasoning for Surgeons

Statistical Reasoning for Surgeons

Trying to read up on statistics can be like trying to decide where you want to start eating the elephant and what’s the most digestible way to get it down. This book is written to give bite-size nuggets of insight based on our experiences grappling with datasets large and small. It is intended to bridge the gap between the formal equations and the practicalities of generating a research manuscript. We won’t pretend reading it will answer all your questions but it will help explain what questions need to be asked for your study and how you can address them with both accuracy and clarity. The size detail and (ostensible) organization of this book allow for easy reading and can give a leg (or at least a half-step) up for those seeking more detailed study later. Features include: Excel sheets to allow exploration of topics raised Emphasis on intuitive explanations over formulas. Consideration of issues specific to clinical and surgical studies Our audience is someone who may or may not have enjoyed formal statistics education (that is you may have had it and not enjoyed it!) who may like seeing a more dressed-down presentation of the topics. Actual statisticians may pick this up at risk of a chuckle (with us or at us) and may find some useful ways to present topics to non-statisticians. | Statistical Reasoning for Surgeons

GBP 48.99
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Algorithms for Next-Generation Sequencing

Algorithms for Next-Generation Sequencing

Advances in sequencing technology have allowed scientists to study the human genome in greater depth and on a larger scale than ever before – as many as hundreds of millions of short reads in the course of a few days. But what are the best ways to deal with this flood of data?Algorithms for Next-Generation Sequencing is an invaluable tool for students and researchers in bioinformatics and computational biology biologists seeking to process and manage the data generated by next-generation sequencing and as a textbook or a self-study resource. In addition to offering an in-depth description of the algorithms for processing sequencing data it also presents useful case studies describing the applications of this technology.

GBP 44.99
1

Image Processing for Cinema

Image Processing for Cinema

Image Processing for Cinema presents a detailed overview of image processing techniques that are used in practice in digital cinema. The book shows how image processing has become ubiquitous in movie-making from shooting to exhibition. It covers all the ways in which image processing algorithms are used to enhance restore adapt and convert moving images. These techniques and algorithms make the images look as good as possible while exploiting the capabilities of cameras projectors and displays. The author focuses on the ideas behind the methods rather than proofs and derivations. The first part of the text presents fundamentals on optics and color. The second part explains how cameras work and details all the image processing algorithms that are applied in-camera. With an emphasis on state-of-the-art methods that are actually used in practice the last part describes image processing algorithms that are applied offline to solve a variety of problems. The book is designed for advanced undergraduate and graduate students in applied mathematics image processing computer science and related fields. It is also suitable for academic researchers and professionals in the movie industry.

GBP 59.99
1

Introductory Concepts for Abstract Mathematics

Introductory Concepts for Abstract Mathematics

Beyond calculus the world of mathematics grows increasingly abstract and places new and challenging demands on those venturing into that realm. As the focus of calculus instruction has become increasingly computational it leaves many students ill prepared for more advanced work that requires the ability to understand and construct proofs. Introductory Concepts for Abstract Mathematics helps readers bridge that gap. It teaches them to work with abstract ideas and develop a facility with definitions theorems and proofs. They learn logical principles and to justify arguments not by what seems right but by strict adherence to principles of logic and proven mathematical assertions - and they learn to write clearly in the language of mathematicsThe author achieves these goals through a methodical treatment of set theory relations and functions and number systems from the natural to the real. He introduces topics not usually addressed at this level including the remarkable concepts of infinite sets and transfinite cardinal numbersIntroductory Concepts for Abstract Mathematics takes readers into the world beyond calculus and ensures their voyage to that world is successful. It imparts a feeling for the beauty of mathematics and its internal harmony and inspires an eagerness and increased enthusiasm for moving forward in the study of mathematics.

GBP 59.99
1

Graphical Methods for Data Analysis

Graphical Methods for Data Analysis

This book present graphical methods for analysing data. Some methods are new and some are old some require a computer and others only paper and pencil; but they are all powerful data analysis tools. In many situations a set of data � even a large set- can be adequately analysed through graphical methods alone. In most other situations a few well-chosen graphical displays can significantly enhance numerical statistical analyses. | Graphical Methods for Data Analysis

GBP 205.00
1

AI for Cars

AI for Cars

Artificial Intelligence (AI) is undoubtedly playing an increasingly significant role in automobile technology. In fact cars inhabit one of just a few domains where you will find many AI innovations packed into a single product. AI for Cars provides a brief guided tour through many different AI landscapes including robotics image and speech processing recommender systems and onto deep learning all within the automobile world. From pedestrian detection to driver monitoring to recommendation engines the book discusses the background research and progress thousands of talented engineers and researchers have achieved thus far and their plans to deploy this life-saving technology all over the world.

GBP 22.99
1

Software Engineering for Science

Software Engineering for Science

Software Engineering for Science provides an in-depth collection of peer-reviewed chapters that describe experiences with applying software engineering practices to the development of scientific software. It provides a better understanding of how software engineering is and should be practiced and which software engineering practices are effective for scientific software. The book starts with a detailed overview of the Scientific Software Lifecycle and a general overview of the scientific software development process. It highlights key issues commonly arising during scientific software development as well as solutions to these problems. The second part of the book provides examples of the use of testing in scientific software development including key issues and challenges. The chapters then describe solutions and case studies aimed at applying testing to scientific software development efforts. The final part of the book provides examples of applying software engineering techniques to scientific software including not only computational modeling but also software for data management and analysis. The authors describe their experiences and lessons learned from developing complex scientific software in different domains. About the EditorsJeffrey Carver is an Associate Professor in the Department of Computer Science at the University of Alabama. He is one of the primary organizers of the workshop series on Software Engineering for Science (http://www. SE4Science. org/workshops). Neil P. Chue Hong is Director of the Software Sustainability Institute at the University of Edinburgh. His research interests include barriers and incentives in research software ecosystems and the role of software as a research object. George K. Thiruvathukal is Professor of Computer Science at Loyola University Chicago and Visiting Faculty at Argonne National Laboratory. His current research is focused on software metrics in open source mathematical and scientific software.

GBP 44.99
1

Introductory Statistics for the Health Sciences

Introductory Statistics for the Health Sciences

Introductory Statistics for the Health Sciences takes students on a journey to a wilderness where science explores the unknown providing students with a strong practical foundation in statistics. Using a color format throughout the book contains engaging figures that illustrate real data sets from published research. Examples come from many areas of the health sciences including medicine nursing pharmacy dentistry and physical therapy but are understandable to students in any field. The book can be used in a first-semester course in a health sciences program or in a service course for undergraduate students who plan to enter a health sciences program. The book begins by explaining the research context for statistics in the health sciences which provides students with a framework for understanding why they need statistics as well as a foundation for the remainder of the text. It emphasizes kinds of variables and their relationships throughout giving a substantive context for descriptive statistics graphs probability inferential statistics and interval estimation. The final chapter organizes the statistical procedures in a decision tree and leads students through a process of assessing research scenarios. Web ResourceThe authors have partnered with William Howard Beasley who created the illustrations in the book to offer all of the data sets graphs and graphing code in an online data repository via GitHub. A dedicated website gives information about the data sets and the authors’ electronic flashcards for iOS and Android devices. These flashcards help students learn new terms and concepts.

GBP 44.99
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Introduction to Python for Humanists

Introduction to Python for Humanists

This book will introduce digital humanists at all levels of education to Python. It provides background and guidance on learning the Python computer programming language and as it presumes no knowledge on the part of the reader about computers or coding concepts allows the reader to gradually learn the more complex tasks that are currently popular in the field of digital humanities. This book will be aimed at undergraduates graduates and faculty who are interested in learning how to use Python as a tool within their workflow. An Introduction to Python for Digital Humanists will act as a primer for students who wish to use Python allowing them to engage with more advanced textbooks. This book fills a real need as it is first Python introduction to be aimed squarely at humanities students as other books currently available do not approach Python from a humanities perspective. It will be designed so that those experienced in Python can teach from it in addition to allowing those who are interested in being self-taught can use it for that purpose. Key Features: Data analysis Data science Computational humanities Digital humanities Python Natural language processing Social network analysis App development | Introduction to Python for Humanists

GBP 44.99
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Mixed Effects Models for Complex Data

Mixed Effects Models for Complex Data

Although standard mixed effects models are useful in a range of studies other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts missing data measurement errors censoring and outliers. For each class of mixed effects model the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data the book introduces linear mixed effects (LME) models generalized linear mixed models (GLMMs) nonlinear mixed effects (NLME) models and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values measurement errors censoring and outliers. Self-contained coverage of specific topicsSubsequent chapters delve more deeply into missing data problems covariate measurement errors and censored responses in mixed effects models. Focusing on incomplete data the book also covers survival and frailty models joint models of survival and longitudinal data robust methods for mixed effects models marginal generalized estimating equation (GEE) models for longitudinal or clustered data and Bayesian methods for mixed effects models. Background materialIn the appendix the author provides background information such as likelihood theory the Gibbs sampler rejection and importance sampling methods numerical integration methods optimization methods bootstrap and matrix algebra. Failure to properly address missing data measurement errors and other issues in statistical analyses can lead

GBP 59.99
1

Probability and Statistics for Computer Scientists

Probability and Statistics for Computer Scientists

Praise for the Second Edition: The author has done his homework on the statistical tools needed for the particular challenges computer scientists encounter. [He] has taken great care to select examples that are interesting and practical for computer scientists. . The content is illustrated with numerous figures and concludes with appendices and an index. The book is erudite and … could work well as a required text for an advanced undergraduate or graduate course. Computing Reviews Probability and Statistics for Computer Scientists Third Edition helps students understand fundamental concepts of Probability and Statistics general methods of stochastic modeling simulation queuing and statistical data analysis; make optimal decisions under uncertainty; model and evaluate computer systems; and prepare for advanced probability-based courses. Written in a lively style with simple language and now including R as well as MATLAB this classroom-tested book can be used for one- or two-semester courses. Features: Axiomatic introduction of probability Expanded coverage of statistical inference and data analysis including estimation and testing Bayesian approach multivariate regression chi-square tests for independence and goodness of fit nonparametric statistics and bootstrap Numerous motivating examples and exercises including computer projects Fully annotated R codes in parallel to MATLAB Applications in computer science software engineering telecommunications and related areas In-Depth yet Accessible Treatment of Computer Science-Related TopicsStarting with the fundamentals of probability the text takes students through topics heavily featured in modern computer science computer engineering software engineering and associated fields such as computer simulations Monte Carlo methods stochastic processes Markov chains queuing theory statistical inference and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). About the Author Michael Baron is David Carroll Professor of Mathematics and Statistics at American University in Washington D. C. He conducts research in sequential analysis and optimal stopping change-point detection Bayesian inference and applications of statistics in epidemiology clinical trials semiconductor manufacturing and other fields. M. Baron is a Fellow of the American Statistical Association and a recipient of the Abraham Wald Prize for the best paper in Sequential Analysis and the Regents Outstanding Teaching Award. M. Baron holds a Ph. D. in statistics from the University of Maryland. In his turn he supervised twelve doctoral students mostly employed on academic and research positions.

GBP 99.99
1

Innovative Methods for Rare Disease Drug Development

Innovative Methods for Rare Disease Drug Development

In the United States a rare disease is defined by the Orphan Drug Act as a disorder or condition that affects fewer than 200 000 persons. For the approval of orphan drug products for rare diseases the traditional approach of power analysis for sample size calculation is not feasible because there are only limited number of subjects available for clinical trials. In this case innovative approaches are needed for providing substantial evidence meeting the same standards for statistical assurance as drugs used to treat common conditions. Innovative Methods for Rare Disease Drug Development focuses on biostatistical applications in terms of design and analysis in pharmaceutical research and development from both regulatory and scientific (statistical) perspectives. Key Features: Reviews critical issues (e. g. endpoint/margin selection sample size requirements and complex innovative design). Provides better understanding of statistical concepts and methods which may be used in regulatory review and approval. Clarifies controversial statistical issues in regulatory review and approval accurately and reliably. Makes recommendations to evaluate rare diseases regulatory submissions. Proposes innovative study designs and statistical methods for rare diseases drug development including n-of-1 trial design adaptive trial design and master protocols like platform trials. Provides insight regarding current regulatory guidance on rare diseases drug development like gene therapy.

GBP 44.99
1