Modeling Operations Research and Business Analytics
This book provides sample exercises, techniques, and solutions to employ mathematical modeling to solve problems in Operations Research and Business Analytics. als.
Mathematical Modeling and Soft Computing in Epidemiology
This book describes the use of different Mathematical Modeling and Soft Computing techniques used in Epidemiology for experiential research in projects such as how infectious diseases progress to show the likely outcome of an epidemic, and to contribute to public health interventions.
Discrete Stochastic Models and Applications for Reliability Engineering and Statistical Quality Control
This book provides real-life examples and illustrations of models in reliability engineering and statistical quality control and establishes a connection between the theoretical framework and their engineering applications.
Pulling Rabbits Out of Hats
Pulling Rabbits Out of Hats: Using Mathematical Modeling in the Material, Biophysical, Fluid Mechanical, and Chemical Sciences focuses on those assumptions made during applied mathematical modeling in which the phenomenological data and the model predictions are self-consistent.
Recent Advances in Time Series Forecasting
Future predictions are always a topic of interest. Precise estimates are crucial in many activities as forecasting errors can lead to significant financial loss. The sequential analysis of data and information gathered from past to present is called time series analysis. This book covers the recent advancements in time series forecasting.
Finitely Presented Groups
This book contains surveys and research articles on the state-of-the-art in finitely presented groups for researchers and graduate students. Overviews of current trends in exponential groups and of the classification of finite triangle groups and finite generalized tetrahedron groups are complemented by new results on a conjecture of Rosenberger and an approximation theorem. A special emphasis is on algorithmic techniques and their complexity, both for finitely generated groups and for finite Z-algebras, including explicit computer calculations highlighting important classical methods. A further chapter surveys connections to mathematical logic, in particular to universal theories of various classes of groups, and contains new results on countable elementary free groups. Applications to cryptography include overviews of techniques based on representations of p-groups and of non-commutative group actions. Further applications of finitely generated groups to topology and artificial intelligence complete the volume. All in all, leading experts provide up-to-date overviews and current trends in combinatorial group theory and its connections to cryptography and other areas.
Statistics of Extremes and Records in Random Sequences
Rare events such as earthquakes, tsunamis, and floods fortunately do not occur every day, but when they do, their effects are devastating. Such rare events are particularly important in understanding and characterizing global warming and climate changes. In addition to natural catastrophes, rare events such as big financial crashes also play a significant role in the economy. In the absence of predictive models, the best way forward is to analyse the statistics of these extreme events and draw conclusions about the probability of their occurrences. Extreme value statistics (EVS) and the statistics of records in a random sequence are examples of a truly interdisciplinary topic, spanning from statistics and mathematics on one side to physics of disordered systems on the other. They have tremendous importance and practical applications in a wide variety of fields, such as climate science, finance, spin-glasses, and random matrices. Statistics and mathematical literature have explored the subject of the classical theory of EVS. However, more recently, EVS started to play a very important role in statistical physics, in particular in disordered systems. This has led to a plethora of activities, both in the statistical physics and in the mathematics communities over the last few decades. This book develops the theory of rare events, both for the classical uncorrelated as well as for correlated sequences, in terms of simple models and examples. Statistics of Extremes and Records in Random Sequences is a pedagogical book with examples illustrating the basic tools and techniques that are essential to a student starting to work in this interesting and rapidly developing field.
Problems & Solutions in Stochastic Calculus with Appln
Problems and Solutions in Stochastic Calculus with Applications exposes readers to simple ideas and proofs in stochastic calculus and its applications. It is intended as a companion to the successful original title Introduction to Stochastic Calculus with Applications (Third Edition) by Fima Klebaner. The current book is authored by three active researchers in the fields of probability, stochastic processes, and their applications in financial mathematics, mathematical biology, and more. The book features problems rooted in their ongoing research. Mathematical finance and biology feature pre-eminently, but the ideas and techniques can equally apply to fields such as engineering and economics.The problems set forth are accessible to students new to the subject, with most of the problems and their solutions centring on a single idea or technique at a time to enhance the ease of learning. While the majority of problems are relatively straightforward, more complex questions are also set in order to challenge the reader as their understanding grows. The book is suitable for either self-study or for instructors, and there are numerous opportunities to generate fresh problems by modifying those presented, facilitating a deeper grasp of the material.
The Polls Weren't Wrong
This book will equip readers with the tools to navigate the mismatch of expectations. It is not intended to replace more technical applications of statistics but is accessible to those interested in learning about how poll data should be understood.
Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine
This volume constitutes the refereed proceedings of the First International Workshop on Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, AIPAD 2024 and the First International Workshop on Personalized Incremental Learning in Medicine, PILM 2024, held in conjunction with MICCAI 2024, in Marrakesh, Morocco, in October 2024. The 8 full papers included in these proceedings were carefully reviewed and selected from 9 submissions. They were organized in topical sections as follows: artificial intelligence in pancreatic disease detection and diagnosis; and personalized incremental learning in medicine.
Study of the impact of factors on returns on financial assets
This dissertation is an internship report for Typhoon Partner, a UK-based proprietary investment firm specializing in the development of quantitative investment strategies. We focus on intra-sector and inter-sector returns relative to the fundamental characteristics of US equities. After a thorough analysis of the basic concepts of risk and the issues involved in risk management, we will first look at the various risk measures associated with each sector (VaR, Expected Shortfall, etc.). Secondly, using the relevant literature, we apply a statistical model to explain cross-asset returns in the equity universe using the financial ratios selected, with the aim of studying the performance of equities within a sector and between several sectors. Finally, we look at the comparison of investment strategies and portfolio insurance.
Writers of the Cold
This study analyses the literary construction of space. More specifically, we analyse the artistic construction of the city and the means used to give it high relevance in the text. To this end, we chose the work of Argentinian writer Jorge Luis Borges, published in the 1920s and 1930s. We also analysed how Vitor Ramil (a reader and self-confessed admirer of Borges) constructs his own Satolep from the city of Pelotas. For this analysis, we used his novels that thematise the city: Pequod (1999) and Satolep (2008).
Introduction to the Spectral Theory of Operators in Spaces with an Indefinite Metric
No detailed description available for "Introduction to the Spectral Theory of Operators in Spaces with an Indefinite Metric".
Mapmatics
"I love maps. I love math. And gosh, do I love this book, which so beautifully and clearly sounds the depths of both." --Ben Orlin, author of Math with Bad Drawings Explore the surprising connections between math and maps--and the myriad ways they've shaped our world and us. Why are coastlines and borders so difficult to measure? How does a UPS driver deliver hundreds of packages in a single day? And where do elusive serial killers hide? The answers lie in the crucial connection between maps and math. In Mapmatics, mathematician Paulina Rowińska leads us on a riveting journey around the globe to discover how maps and math are deeply entwined, and always have been. From a sixteenth-century map, an indispensable navigation tool that exaggerates the size of northern countries, to public transport maps that both guide and confound passengers, to congressional maps that can empower or silence whole communities, she reveals how maps and math have shaped not only our sense of space but our worldview. In her hands, we learn how to read maps like a mathematician--to extract richer information and, just as importantly, to question our conclusions by asking what we don't see. Written with authority and compassion, wit and unforgettable storytelling, this is math exposition at its best. By unpacking the math behind the maps we depend on, Mapmatics illuminates how our world works and, ultimately, how we can better look after it.
Markov Decision Problems with Countable State Spaces
No detailed description available for "Markov Decision Problems with Countable State Spaces".
Likelihood Methods in Survival Analysis
Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects.Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes.Features Provides a broad and accessible overview of likelihood methods in survival analysis Covers a wide range of data types and models, from the semi-parametric Cox model with interval censoring through to parametric survival models for competing risks Includes many examples using real data to illustrate the methods Includes integrated R code for implementation of the methods Supplemented by a GitHub repository with datasets and R code The book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.
Statistical Methods Using SPSS
Statistical Methods Using SPSS provides a practical approach for better understanding of the advanced statistical concepts that are applied in business, economics, epidemiology, public health, agriculture and other areas of data analytics.
Fundamental Statistical Concepts and Techniques in the Biological and Environmental Sciences
Fundamental Statistical Concepts and Techniques in the Biological and Environmental Sciences: With jamovi is an introductory textbook for learning statistics. It starts with the very basics and prioritises helping the reader to develop a conceptual understanding of statistics, and apply the most fundamental statistical tools. New concepts are introduced with examples designed to be familiar to the reader, serving as a useful starting point for exploring more abstract concepts.Key Features: Designed to be accessible for students learning statistics in biological and environmental sciences. Utilizes the statistical software jamovi to explore new concepts. Prioritizes good statistical judgement over adherence to protocols. This book will be useful to students beginning their study of statistical concepts in biological and environmental sciences, whilst also acting as an insightful resource for teachers using jamovi in the classroom. Fundamental Statistical Concepts and Techniques in the Biological and Environmental Sciences: With jamovi is a valuable resource for anyone who wishes to understand and apply statistical techniques commonly used in the biological and environmental sciences.
An Introduction to Complex Analysis and the Laplace Transform
The textbook is designed for university students studying science and engineering. I have aimed for a comparatively short book that would give a sufficiently full exposition of the fundamentals of the theory of functions of a complex variable to prepare the student for various applications.
Functional Analysis
This comprehensive introduction to functional analysis covers both the abstract theory and applications to spectral theory, the theory of partial differential equations, and quantum mechanics. It starts with the basic results of the subject and progresses towards a treatment of several advanced topics not commonly found in functional analysis textbooks, including Fredholm theory, form methods, boundary value problems, semigroup theory, trace formulas, and a mathematical treatment of states and observables in quantum mechanics. The book is accessible to graduate students with basic knowledge of topology, real and complex analysis, and measure theory. With carefully written out proofs, more than 300 problems, and appendices covering the prerequisites, this self-contained volume can be used as a text for various courses at the graduate level and as a reference text for researchers in the field.
Applied Regression and ANOVA Using SAS
Designed for researchers primarily interested in what their data are revealing, this book presents statistical methods without burdening readers with matrix algebra and calculus. The book shows how high resolution, publication-ready graphics associated with regression and ANOVA methods are produced with virtually no effort by the SAS user.
Hybrid Frequentist/Bayesian Power and Bayesian Power in Planning Clinical Trials
This book provides a practical introduction to unconditional approaches to planning randomised clinical trials, particularly aimed at drug development in the pharmaceutical industry. This book is aimed at providing guidance to practitioners in using average power, assurance and related concepts.
Nicknames, nicknames and nicknames in the Tumaque簽a Speech
The Colombian Pacific is one of the largest and most biodiverse territories in the country. It is inhabited by Afro-descendants and indigenous people from different groups, but has little or no relevant and inclusive education. Here, we intend to reflect on the importance of ethno-educational work that starts from the speech and oral tradition to reach the knowledge of Afro-Pacific culture from elementary school, showing the obvious importance of endogenous ethnopedagogical work to achieve cementing ethnic and cultural identity in the early stages of academic life.
Introduction to Data Science
Unlike the first edition, the new edition has been split into two books.Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis challenges. These include R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation with Quarto and knitr. The new edition includes additional material on data.table, locales, and accessing data through APIs. The book is divided into four parts: R, Data Visualization, Data Wrangling, and Productivity Tools. Each part has several chapters meant to be presented as one lecture and includes dozens of exercises. The second book will cover topics including probability, statistics and prediction algorithms with R.Throughout the book, we use motivating case studies. In each case study, we try to realistically mimic a data scientist's experience. For each of the skills covered, we start by asking specific questions and answer these through data analysis. Examples of the case studies included in the book are: US murder rates by state, self-reported student heights, trends in world health and economics, and the impact of vaccines on infectious disease rates.This book is meant to be a textbook for a first course in Data Science. No previous knowledge of R is necessary, although some experience with programming may be helpful. To be a successful data analyst implementing these skills covered in this book requires understanding advanced statistical concepts, such as those covered the second book. If you read and understand all the chapters and complete all the exercises in this book, and understand statistical concepts, you will be well-positioned to perform basic data analysis tasks and you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Cleaning Up the Human Skeleton and Genome
This is a work traversed by scientific and technical themes in which the indispensable irrationality interacts: African Art and Spirituality (an element of stability in any science so that the latter does not cross the boundaries of ethics, morality... I reread this precious thesis with great interest, during which it is not so easy to tackle all the themes, analyses and reflections. The skilful and ingenious researcher Audrey KIBAMBA had a head start, for he constructed his thesis in such a way that all the themes dealt with converged like guidelines towards the same vanishing point. In biology, we know that bacteria cooperate, exchange information, evolve and even advance their ability to evolve. Ants do the same. Human beings must do the same too, to evolve better in this fast-paced, dizzying world. This world is making spectacular progress in many areas, and we are in the midst of a major transition in our evolution.
Trustworthy Artificial Intelligence for Healthcare
This book constitutes the proceedings of Second International Workshop on Trustworthy Artificial Intelligence for Healthcare, TAI4H 2024, held in Jeju, South Korea, in August 2024, in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI 2024. The 13 full papers included in this book were carefully reviewed and selected from 21 submissions. They focus on trustworthy artificial intelligence, healthcare, generalization, explainability, fairness, privacy, multi-modal fusion, foundation models.
An Introduction to Panel Data QCA in R
This book discusses and compares four different approaches towards analyzing panel data in QCA. It introduces QCA as a research approach, then discusses the most important assumptions, and steps like set-calibration and theory-testing, and demonstrates each of the four panel data approaches.
Fundamentals of Probability
Praise for the fourth edition: "This book is an excellent primer on probability .... The flow of the text aids its readability, and the book is indeed a treasure trove of set and solved problems. --Dalia Chakrabarty, Brunel University, UK "This textbook provides a thorough and rigorous treatment of fundamental probability, including both discrete and continuous cases. The book's ample collection of exercises gives instructors and students a great deal of practice and tools to sharpen their understanding." --Joshua Stangle, University of Wisconsin - Superior, USA This one- or two-term calculus-based basic probability text is written for majors in mathematics, physical sciences, engineering, statistics, actuarial science, business and finance, operations research, and computer science. It presents probability in a natural way: through interesting and instructive examples and exercises that motivate the theory, definitions, theorems, and methodology. This book is mathematically rigorous and, at the same time, closely matches the historical development of probability. Whenever appropriate, historical remarks are included, and the 2096 examples and exercises have been carefully designed to arouse curiosity and hence encourage students to delve into the theory with enthusiasm. New to the Fifth Edition: In this edition, a significant change has been made in the order of material presentation. The topics such as the joint probability mass function, joint probability density functions, independence of random variables, sums of random variables, the central limit theorem, and certain other materials have been covered earlier in the book, enabling students to grasp these crucial concepts from the start. These changes have considerable merit, particularly the idea of covering the celebrated central limit theorem immediately after discussing the normal distribution. Additionally, discussions on sigma fields are provided and an in-depth section on characteristic functions is added. The central limit theorem has been proven using both moment-generating functions and characteristic functions. In the present edition, numerous new figures are included that were drawn for the first time, specifically to aid in students' understanding of the material. These fresh illustrations, along with all the previous ones in the book, have been meticulously crafted by the technical support team at CRC. Instructors who prefer the content arrangement used in previous editions can still teach the material in the same order as those editions. Moreover, the homepage of this book contains a whole chapter with comprehensive coverage on Stochastic Processes as well as additional contents for Chapters 1 to 10, such as extra examples, supplementary topics, and practical applications to facilitate in-depth exploration. Furthermore, it offers thorough solutions for all self-tests and self-quiz problems, empowering students to assess their progress and grasp of this demanding subject. In this new edition, at the end of select chapters, sections are included dedicated to exploring approximate solutions for complex probabilistic problems using simulation techniques. These simulations are conducted using the R software, a powerful tool well-suited for probabilistic simulations due to its extensive collection of built-in functions and numerous specialized libraries designed for various simulation purposes. In the homepage of the book, a chapter, titled "Algorithm-Driven Simulations," is presented in which we delve deeply into the concept of simulation using algorithms exclusively, without being tied to any specific programming language.
Introduction to Probability and Statistics
This book aims to be a manual for understanding problems in probability and statistics. The main emphasis is the random variables (discrete and continuous), their distributions, the basics of statistics, the law of large numbers, the central limit theorem, statistical models, least squares estimation, confidence intervals for the mean, testing hypothesis, and statistics with datasets.The book is aimed at junior students in the field of applied mathematics, physics, computer engineering, economics, artificial intelligence, and data science & analytics in science and engineering disciplines.
Game-Theoretical Models in Biology
Covering the major topics of evolutionary game theory, this book presents both abstract and practical mathematical models of real biological situations. It discusses the static aspects of game theory in a mathematically rigorous way that is appealing to mathematicians. The text is also useful to biologists.
Handbook of Complex Analysis
The Handbook of Complex Analysis will be an entree for advanced undergraduates and beginning graduate students in the subject of complex analysis. The subject of complex analysis of increasing importance. Even the function theory of several complex variables has seen applications in cosmology, geophysics, and engineering.
Applied Statistical Modelling for Ecologists
**2025 PROSE Award Finalist in Environmental Science** Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand. This book is the successor of the widely used Introduction to WinBUGS for Ecologists (K矇ry, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks. The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB.
An Introduction to Scientific Computing with MATLAB(R) and Python Tutorials
This textbook covers essential numerical techniques including basic methods for linear systems, root finding, interpolation, numerical integration and differentiation, least squares and Monte Carlo methods. This book balances the development, implementation, application and analysis of computational ideas and methods.
Data Science
Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.
Partial Differential Equations
Designed for a one-semester course, this text provides a bridge between the standard PDE course for undergraduate students in science and engineering and the PDE course for graduate students in mathematics who are pursuing a research career in analysis.
Introduction to Advanced Engineering Mathematics and Simulation
Introduction to Advanced Engineering Mathematics and Simulation in MATLAB Applications Approach has been prepared for instructors, learners and researchers interested in Advanced Engineering Mathematics and Simulation. The resource has emphasis on practical problems formulation and solving in Engineering. Interest is specifically guiding on developing solutions using the mathematical formulas and codes in MATLAB.
Probability Theory and Statistics with Real World Applications
The idea of the book is to present a text that is useful for both students of quantitative sciences and practitioners who work with univariate or multivariate probabilistic models. Since the text should also be suitable for self-study, excessive formalism is avoided though mathematical rigor is retained. A deeper insight into the topics is provided by detailed examples and illustrations. The book covers the standard content of a course in probability and statistics. However, the second edition includes two new chapters about distribution theory and exploratory data analysis. The first-mentioned chapter certainly goes beyond the standard material. It is presented to reflect the growing practical importance of developing new distributions. The second new chapter studies intensively one- and bidimensional concepts like assymetry, kurtosis, correlation and determination coefficients. In particular, examples are intended to enable the reader to take a critical look at the appropriateness of the geometrically motivated concepts.
Numerical Methods and Analysis with Mathematical Modelling
What sets this book apart is the modeling aspects utilizing numerical analysis (methods) to obtain solutions. The authors cover the basic numerical analysis methods first with simple examples to illustrate the techniques and discuss possible errors.
Fundamentals of Fourier Analysis
This self-contained text introduces Euclidean Fourier Analysis to graduate students who have completed courses in Real Analysis and Complex Variables. It provides sufficient content for a two course sequence in Fourier Analysis or Harmonic Analysis at the graduate level. In true pedagogical spirit, each chapter presents a valuable selection of exercises with targeted hints that will assist the reader in the development of research skills. Proofs are presented with care and attention to detail. Examples are provided to enrich understanding and improve overall comprehension of the material. Carefully drawn illustrations build intuition in the proofs. Appendices contain background material for those that need to review key concepts. Compared with the author's other GTM volumes (Classical Fourier Analysis and Modern Fourier Analysis), this text offers a more classroom-friendly approach as it contains shorter sections, more refined proofs, and a wider range of exercises. Topics include the Fourier Transform, Multipliers, Singular Integrals, Littlewood-Paley Theory, BMO, Hardy Spaces, and Weighted Estimates, and can be easily covered within two semesters.
From Numbers to Knowledge
This book explores the transformative journey of data analysis, beginning with foundational concepts like data collection and cleaning, progressing through exploratory techniques such as descriptive statistics and data visualization, and culminating in advanced methodologies including inferential statistics, predictive modeling, and machine learning. It emphasizes the importance of data quality, effective analysis techniques, and the application of insights to drive informed decision-making across various domains. Through practical examples and case studies, it equips readers with the tools and knowledge to harness the power of data for meaningful impact and innovation.
Geographic Information, Geospatial Technologies and Spatial Data Science for Health
In this book, epidemiology and public health are integrated with spatial data science to examine health issues in dynamically changing environments. This is too broad a field to be covered in one book and so it has been necessary to be selective with the topics, methods and examples.
Modern Statistics with R
The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit.
Observability and Mathematics
The author approaches an old classic problem - the existence of solutions of Navier-Stokes eqs. The main objective is to model and derive of equation of continuity, Euler equation of fluid motion, energy flux equation, Navier-Stokes eqs from the observer point of view and solve classic problem for this interpretation of fluid motion laws.
Real Analysis and Foundations
This new edition continues the effort to make the book accessible to a broader audience. Many students who take a real analysis course do not have the ideal background. The new edition offers chapters on background material like set theory, logic, and methods of proof. The more advanced material in the book is made more apparent.
Medical Statistics for Cancer Studies
This textbook shows how cancer data can be analysed in a variety of ways, covering cancer clinical trial data, epidemiological data, biological data, and genetic data. It provides detailed overviews of survival analysis, clinical trials, regression analysis, epidemiology, meta-analysis, biomarkers, and cancer informatics.
Real-World Evidence in a Patient-Centric Digital Era
This book will be a vital reference for medical researchers, health technology innovators, data scientists, epidemiologists, population health analysts, health economists, outcomes researchers, as well as policymakers and analysts in the healthcare industry.
Numerical Analysis and Scientific Computation
This is an introductory single-term numerical analysis text with a modern scientific computing flavor. It offers an immediate immersion in numerical methods featuring an up-to-date approach to computational matrix algebra and methods used in actual software packages, highlighting how hardware concerns can impact the choice of algorithm.
