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Bioinformatics A Practical Guide to NCBI Databases and Sequence Alignments

Bioinformatics A Practical Guide to NCBI Databases and Sequence Alignments

Bioinformatics: A Practical Guide to NCBI Databases and Sequence Alignments provides the basics of bioinformatics and in-depth coverage of NCBI databases sequence alignment and NCBI Sequence Local Alignment Search Tool (BLAST). As bioinformatics has become essential for life sciences the book has been written specifically to address the need of a large audience including undergraduates graduates researchers healthcare professionals and bioinformatics professors who need to use the NCBI databases retrieve data from them and use BLAST to find evolutionarily related sequences sequence annotation construction of phylogenetic tree and the conservative domain of a protein to name just a few. Technical details of alignment algorithms are explained with a minimum use of mathematical formulas and with graphical illustrations. Key Features Provides readers with the most-used bioinformatics knowledge of bioinformatics databases and alignments including both theory and application via illustrations and worked examples. Discusses the use of Windows Command Prompt Linux shell R and Python for both Entrez databases and BLAST. The companion website (http://www. hamiddi. com/instructors/) contains tutorials R and Python codes instructor materials including slides exercises and problems for students. This is the ideal textbook for bioinformatics courses taken by students of life sciences and for researchers wishing to develop their knowledge of bioinformatics to facilitate their own research. | Bioinformatics A Practical Guide to NCBI Databases and Sequence Alignments

GBP 82.99
1

Semigroups of Bounded Operators and Second-Order Elliptic and Parabolic Partial Differential Equations

Semigroups of Bounded Operators and Second-Order Elliptic and Parabolic Partial Differential Equations

Semigroups of Bounded Operators and Second-Order Elliptic and Parabolic Partial Differential Equations aims to propose a unified approach to elliptic and parabolic equations with bounded and smooth coefficients. The book will highlight the connections between these equations and the theory of semigroups of operators while demonstrating how the theory of semigroups represents a powerful tool to analyze general parabolic equations. Features Useful for students and researchers as an introduction to the field of partial differential equations of elliptic and parabolic types Introduces the reader to the theory of operator semigroups as a tool for the analysis of partial differential equations

GBP 180.00
1

A Handbook of Statistical Analyses using R

A Handbook of Statistical Analyses using R

Like the best-selling first two editions A Handbook of Statistical Analyses using R Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses from simple inference to recursive partitioning to cluster analysis. New to the Third Edition Three new chapters on quantile regression missing values and Bayesian inference Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables Additional exercises More detailed explanations of R code New section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3) which includes some slides that can be used in introductory statistics courses Whether you‘re a data analyst scientist or student this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples it emphasizes the practical application and interpretation of results.

GBP 180.00
1

Design and Analysis of Experiments and Observational Studies using R

Design and Analysis of Experiments and Observational Studies using R

Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected. Features: Classical experimental design with an emphasis on computation using tidyverse packages in R. Applications of experimental design to clinical trials A/B testing and other modern examples. Discussion of the link between classical experimental design and causal inference. The role of randomization in experimental design and sampling in the big data era. Exercises with solutions. Instructor slides in RMarkdown a new R package will be developed to be used with book and a bookdown version of the book will be freely available. The proposed book will emphasize ethics communication and decision making as part of design data analysis and statistical thinking. | Design and Analysis of Experiments and Observational Studies using R

GBP 82.99
1

Applied Stochastic Modelling

Applied Stochastic Modelling

Highlighting modern computational methods Applied Stochastic Modelling Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and improved figures this edition offers numerous updates throughout. New to the Second Edition An extended discussion on Bayesian methods A large number of new exercises A new appendix on computational methods The book covers both contemporary and classical aspects of statistics including survival analysis Kernel density estimation Markov chain Monte Carlo hypothesis testing regression bootstrap and generalised linear models. Although the book can be used without reference to computational programs the author provides the option of using powerful computational tools for stochastic modelling. All of the data sets and MATLAB and R programs found in the text as well as lecture slides and other ancillary material are available for download at www. crcpress. com Continuing in the bestselling tradition of its predecessor this textbook remains an excellent resource for teaching students how to fit stochastic models to data.

GBP 175.00
1

Bounds for Determinants of Linear Operators and their Applications

Bounds for Determinants of Linear Operators and their Applications

This book deals with the determinants of linear operators in Euclidean Hilbert and Banach spaces. Determinants of operators give us an important tool for solving linear equations and invertibility conditions for linear operators enable us to describe the spectra to evaluate the multiplicities of eigenvalues etc. We derive upper and lower bounds and perturbation results for determinants and discuss applications of our theoretical results to spectrum perturbations matrix equations two parameter eigenvalue problems as well as to differential difference and functional-differential equations. | Bounds for Determinants of Linear Operators and their Applications

GBP 150.00
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The New S Language

The New S Language

This book provides documentation for a new version of the S system released in 1988. The new S enhances the features that have made S popular: interactive computing flexible graphics data management and a large collection of functions. The new S features make possible new applications and higher-level programming including a single unified language user defined functions as first-class objects symbolic computations more accurate numerical calculations and a new approach to graphics. S now provides direct interfaces to the poowerful tool of the UNIX operating system and to algorithms implemented in Fortran and C. | The New S Language

GBP 325.00
1

Multilevel Modeling Using Mplus

Multilevel Modeling Using Mplus

This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts using Mplus as the software tool and demonstrating the various functions available for these analyses in Mplus which is widely used by researchers in various fields including most of the social sciences. In particular Mplus offers users a wide array of tools for latent variable modelling including for multilevel data. | Multilevel Modeling Using Mplus

GBP 180.00
1

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

Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)

Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)

Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form called “ADAM” (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models explaining how a variety of instruments can be used to solve real life problems. At the moment there is no other tool in R or Python that would be able to model both intermittent and regular demand would support both ETS and ARIMA work with explanatory variables be able to deal with multiple seasonalities (e. g. for hourly demand data) and have a support for automatic selection of orders components and variables and provide tools for diagnostics and further improvement of the estimated model. ADAM can do all of that in one and the same framework. Given the rising interest in forecasting ADAM being able to do all those things is a useful tool for data scientists business analysts and machine learning experts who work with time series as well as any researchers working in the area of dynamic models. Key Features: • It covers basics of forecasting • It discusses ETS and ARIMA models • It has chapters on extensions of ETS and ARIMA including how to use explanatory variables and how to capture multiple frequencies • It discusses intermittent demand and scale models for ETS ARIMA and regression • It covers diagnostics tools for ADAM and how to produce forecasts with it • It does all of that with examples in R.

GBP 89.99
1

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security

Introduction to Machine Learning with Applications in Information Security Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques reinforced via realistic applications. The book is accessible and doesn’t prove theorems or dwell on mathematical theory. The goal is to present topics at an intuitive level with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth including Hidden Markov Models (HMM) Support Vector Machines (SVM) and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN) boosting Random Forests and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation Convolutional Neural Networks (CNN) Multilayer Perceptrons (MLP) and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented including Long Short-Term Memory (LSTM) Generative Adversarial Networks (GAN) Extreme Learning Machines (ELM) Residual Networks (ResNet) Deep Belief Networks (DBN) Bidirectional Encoder Representations from Transformers (BERT) and Word2Vec. Finally several cutting-edge deep learning topics are discussed including dropout regularization attention explainability and adversarial attacks. Most of the examples in the book are drawn from the field of information security with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming and elementary computing concepts are assumed in a few of the application sections. However anyone with a modest amount of computing experience should have no trouble with this aspect of the book. Instructor resources including PowerPoint slides lecture videos and other relevant material are provided on an accompanying website: http://www. cs. sjsu. edu/~stamp/ML/.

GBP 62.99
1

Grid Computing Techniques and Applications

Grid Computing Techniques and Applications

Designed for senior undergraduate and first-year graduate students Grid Computing: Techniques and Applications shows professors how to teach this subject in a practical way. Extensively classroom-tested it covers job submission and scheduling Grid security Grid computing services and software tools graphical user interfaces workflow editors and Grid-enabling applications. The book begins with an introduction that discusses the use of a Grid computing Web-based portal. It then examines the underlying action of job submission using a command-line interface and the use of a job scheduler. After describing both general Internet security techniques and specific security mechanisms developed for Grid computing the author focuses on Web services technologies and how they are adopted for Grid computing. He also discusses the advantages of using a graphical user interface over a command-line interface and presents a graphical workflow editor that enables users to compose sequences of computational tasks visually using a simple drag-and-drop interface. The final chapter explains how to deploy applications on a Grid. The Grid computing platform offers much more than simply running an application at a remote site. It also enables multiple geographically distributed computers to collectively obtain increased speed and fault tolerance. Illustrating this kind of resource discovery this practical text encompasses the varied and interconnected aspects of Grid computing including how to design a system infrastructure and Grid portal. Supplemental Web ResourcesThe author’s Web site offers various instructional resources including slides and links to software for programming assignments. Many of these assignments do not require access to a Grid platform. Instead the author provides step-by-step instructions for installing open-source software to deploy and test Web and Grid services a Grid computing workflow editor to design and test workflows and a Grid computing portal to deploy portlets. | Grid Computing Techniques and Applications

GBP 69.99
1

Anyone Can Code The Art and Science of Logical Creativity

Anyone Can Code The Art and Science of Logical Creativity

Anyone Can Code: The Art and Science of Logical Creativity introduces computer programming as a way of problem-solving through logical thinking. It uses the notion of modularization as a central lens through which we can make sense of many software concepts. This book takes the reader through fundamental concepts in programming by illustrating them in three different and distinct languages: C/C++ Python and Javascript. Key features: Focuses on problem-solving and algorithmic thinking instead of programming functions syntax and libraries; Includes engaging examples including video games and visual effects; Provides exercises and reflective questions. This book gives beginner and intermediate learners a strong understanding of what they are doing so that they can do it better and with any other tool or language that they may end up using later. | Anyone Can Code The Art and Science of Logical Creativity

GBP 56.99
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Elements of Parallel Computing

Elements of Parallel Computing

Created to help scientists and engineers write computer code this practical book addresses the important tools and techniques that are necessary for scientific computing but which are not yet commonplace in science and engineering curricula. This book contains chapters summarizing the most important topics that computational researchers need to know about. It leverages the viewpoints of passionate experts involved with scientific computing courses around the globe and aims to be a starting point for new computational scientists and a reference for the experienced. Each contributed chapter focuses on a specific tool or skill providing the content needed to provide a working knowledge of the topic in about one day. While many individual books on specific computing topics exist none is explicitly focused on getting technical professionals and students up and running immediately across a variety of computational areas. | Elements of Parallel Computing

GBP 180.00
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Fundamentals of Causal Inference With R

Fundamentals of Causal Inference With R

Overall this textbook is a perfect guide for interested researchers and students who wish to understand the rationale and methods of causal inference. Each chapter provides an R implementation of the introduced causal concepts and models and concludes with appropriate exercises. An-Shun Tai & Sheng-Hsuan Lin in BiometricsOne of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is of utmost importance. Fundamentals of Causal Inference explains and relates different methods of confounding adjustment in terms of potential outcomes and graphical models including standardization difference-in-differences estimation the front-door method instrumental variables estimation and propensity score methods. It also covers effect-measure modification precision variables mediation analyses and time-dependent confounding. Several real data examples simulation studies and analyses using R motivate the methods throughout. The book assumes familiarity with basic statistics and probability regression and R and is suitable for seniors or graduate students in statistics biostatistics and data science as well as PhD students in a wide variety of other disciplines including epidemiology pharmacy the health sciences education and the social economic and behavioral sciences. Beginning with a brief history and a review of essential elements of probability and statistics a unique feature of the book is its focus on real and simulated datasets with all binary variables to reduce complex methods down to their fundamentals. Calculus is not required but a willingness to tackle mathematical notation difficult concepts and intricate logical arguments is essential. While many real data examples are included the book also features the Double What-If Study based on simulated data with known causal mechanisms in the belief that the methods are best understood in circumstances where they are known to either succeed or fail. Datasets R code and solutions to odd-numbered exercises are available on the book's website at www. routledge. com/9780367705053. Instructors can also find slides based on the book and a full solutions manual under 'Instructor Resources'. | Fundamentals of Causal Inference With R

GBP 56.99
1

Multiple Imputation in Practice With Examples Using IVEware

Multiple Imputation in Practice With Examples Using IVEware

Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis from simple to complex problems using real and simulated data sets. Data sets from cross-sectional retrospective prospective and longitudinal studies randomized clinical trials complex sample surveys are used to illustrate both simple and complex analyses. Version 0. 3 of IVEware the software developed by the University of Michigan is used to illustrate analyses. IVEware can multiply impute missing values analyze multiply imputed data sets incorporate complex sample design features and be used for other statistical analyses framed as missing data problems. IVEware can be used under Windows Linux and Mac and with software packages like SAS SPSS Stata and R or as a stand-alone tool. This book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems along with examples of correct analysis techniques. | Multiple Imputation in Practice With Examples Using IVEware

GBP 44.99
1

Piece-wise and Max-Type Difference Equations Periodic and Eventually Periodic Solutions

Piece-wise and Max-Type Difference Equations Periodic and Eventually Periodic Solutions

Piece-wise and Max-Type Difference Equations: Periodic and Eventually Periodic Solutions is intended for lower-level undergraduate students studying discrete mathematics. The book focuses on sequences as recursive relations and then transitions to periodic recursive patterns and eventually periodic recursive patterns. In addition to this it will also focus on determining the patterns of periodic and eventually periodic solutions inductively. The aim of the author throughout this book is to get students to understand the significance of pattern recognition as a mathematical tool. Key Features Can provide possible topics for undergraduate research and for bachelor’s thesis Provides supplementary practice problems and some open-ended research problems at the end of each chapter Focusses on determining the patterns of periodic and eventually periodic solutions inductively Enhances students’ algebra skills before moving forward to upper level courses Familiarize students with the topics before they start undergraduate research by providing applications. | Piece-wise and Max-Type Difference Equations Periodic and Eventually Periodic Solutions

GBP 44.99
1

Quantitative Finance with Python A Practical Guide to Investment Management Trading and Financial Engineering

Quantitative Finance with Python A Practical Guide to Investment Management Trading and Financial Engineering

Quantitative Finance with Python: A Practical Guide to Investment Management Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on rigorous introduction to foundational topics in quant finance such as options pricing portfolio optimization and machine learning. Simultaneously the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs such as those in master’s programs in Mathematical Finance Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques and in particular covers some introductory concepts of Machine Learning. Free-to-access repository with Python codes available at www. routledge. com/ 9781032014432 and on https://github. com/lingyixu/Quant-Finance-With-Python-Code. | Quantitative Finance with Python A Practical Guide to Investment Management Trading and Financial Engineering

GBP 99.99
1

Tree-Based Methods for Statistical Learning in R

Tree-Based Methods for Statistical Learning in R

Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit) and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e. g. Python Spark and Julia) and example usage on real data sets. While the book mostly uses R it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage from the ground up of tree-based methods (e. g. CART conditional inference trees bagging boosting and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package called treemisc which contains several data sets and functions used throughout the book (e. g. there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations) or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining or even improving performance.

GBP 82.99
1

Solution Techniques for Elementary Partial Differential Equations

Solution Techniques for Elementary Partial Differential Equations

In my opinion this is quite simply the best book of its kind that I have seen thus far. —Professor Peter Schiavone University of Alberta from the Foreword to the Fourth Edition Praise for the previous editions An ideal tool for students taking a first course in PDEs as well as for the lecturers who teach such courses. —Marian Aron Plymouth University UK This is one of the best books on elementary PDEs this reviewer has read so far. Highly recommended. —CHOICE Solution Techniques for Elementary Partial Differential Equations Fourth Edition remains a top choice for a standard undergraduate-level course on partial differential equations (PDEs). It provides a streamlined direct approach to developing students’ competence in solving PDEs and offers concise easily understood explanations and worked examples that enable students to see the techniques in action. New to the Fourth Edition Two additional sections A larger number and variety of worked examples and exercises A companion pdf file containing more detailed worked examples to supplement those in the book which can be used in the classroom and as an aid to online teaching

GBP 56.99
1

Direct and Projective Limits of Geometric Banach Structures

Direct and Projective Limits of Geometric Banach Structures

This book describes in detail the basic context of the Banach setting and the most important Lie structures found in finite dimension. The authors expose these concepts in the convenient framework which is a common context for projective and direct limits of Banach structures. The book presents sufficient conditions under which these structures exist by passing to such limits. In fact such limits appear naturally in many mathematical and physical domains. Many examples in various fields illustrate the different concepts introduced. Many geometric structures existing in the Banach setting are stable by passing to projective and direct limits with adequate conditions. The convenient framework is used as a common context for such types of limits. The contents of this book can be considered as an introduction to differential geometry in infinite dimension but also a way for new research topics. This book allows the intended audience to understand the extension to the Banach framework of various topics in finite dimensional differential geometry and moreover the properties preserved by passing to projective and direct limits of such structures as a tool in different fields of research. | Direct and Projective Limits of Geometric Banach Structures

GBP 120.00
1

Modeling and Simulation in Python

Modeling and Simulation in Python

The use of Python as a powerful computational tool is expanding with great strides. Python is a language which is easy to use and the libraries of tools provides it with efficient versatility. As the tools continue to expand users can create insightful models and simulations. While the tools offer an easy method to create a pipeline such constructions are not guaranteed to provide correct results. A lot of things can go wrong when building a simulation - deviously so. Users need to understand more than just how to build a process pipeline. Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments and includes a number of interesting examples including Shakespeare movie databases virus spread and Chess. Key Features: Several theories and applications are provided each with working Python scripts. All Python functions written for this book are archived on GitHub. Readers do not have to be Python experts but a working knowledge of the language is required. Students who want to know more about the foundations of modeling and simulation will find this an educational and foundational resource.

GBP 82.99
1

Data Science in Practice

Data Science in Practice

Data Science in Practice is the ideal introduction to data science. With or without math skills here you get the all-round view that you need for your projects. This book describes how to properly question data in order to unearth the treasure that data can be. You will get to know the relevant analysis methods and will be introduced to the programming language R which is ideally suited for data analysis. Associated tools like notebooks that make data science programming easily accessible are included in this introduction. Because technology alone is not enough this book also deals with problems in project implementation illuminates various fields of application and does not forget to address ethical aspects. Data Science in Practice includes many examples notes on errors decision-making aids and other practical tips. This book is ideal as a complementary text for university students and is a useful learning tool for those moving into more data-related roles. Key Features: Success factors and tools for all project phases Includes application examples for various subject areas Introduces many aspects of Data Science from requirements analysis to data acquisition and visualization

GBP 54.99
1

Producing High-Quality Figures Using SAS/GRAPH and ODS Graphics Procedures

Producing High-Quality Figures Using SAS/GRAPH and ODS Graphics Procedures

Create Top-Quality Figures Using SAS Producing High-Quality Figures Using SAS/GRAPH and ODS Graphics Procedures gives statisticians and SAS programmers practical guidance on presenting research data in high-quality figures that meet the publication requirements of academic institutions and various industries such as pharmaceutical companies agricultural businesses and financial organizations. Choose the Proper Graph Formats Options and Fonts The book provides all the details on selecting the right figure formats options and fonts to produce high-quality figures. In-depth instructions and SAS programs using procedures in both SAS/GRAPH and ODS Graphics show how to generate sample figures in listing graphics formats and ODS document files. Each chapter includes practical examples and SAS programs. Readers can easily modify the SAS programs to develop high-quality figures to meet their own needs. For those unfamiliar with SAS programming and SAS Graphics the author describes how to produce sample figures in SAS ODS Graphics designer a convenient tool for generating figures without having to write SAS programs. The book also discusses how to produce figures in a SAS Enterprise Guide project. | Producing High-Quality Figures Using SAS/GRAPH® and ODS Graphics Procedures

GBP 160.00
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