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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

Extreme Value Modeling and Risk Analysis Methods and Applications

Extreme Value Modeling and Risk Analysis Methods and Applications

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes the book explains univariate extreme value mixture modeling threshold selection in extreme value analysis and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas develops time series of extremes with applications from climatology describes max-autoregressive and moving maxima models for extremes and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies such as composite likelihood Bayesian inference and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling including financial investments insurance and financial risk management weather and climate disasters clinical trials and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology hydrology finance insurance sports and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists. | Extreme Value Modeling and Risk Analysis Methods and Applications

GBP 44.99
1

CRC Standard Mathematical Tables and Formulas

CRC Standard Mathematical Tables and Formulas

Containing more than 6 000 entries CRC Standard Mathematical Tables and Formulas 33rd Edition continues to provide essential formulas tables figures and detailed descriptions. The newest edition of this popular series also features many diagrams group tables and integrals that are not available online. This edition also incorporates important topics such as max plus algebra financial options pseudospectra and proof methods. Newly updated topics reflecting new results include couple analogues radar and significant equations of mathematics. New features of the 33rd edition include: Larger trim size five new topics and topics which have been modified to update results Provides practical ready-to-use information and covers important topics that are unfamiliar to many readers such as visual proofs and sequences Includes hard-to-find and more complete information than found in the Internet such as table of conformal mappings and integral tables Adds descriptions of new functions: Lambert prolate spheroidal and Weierstrass Even though the book has been updated it retains the same successful format of previous editions in that material is still presented in a multi-sectional format.

GBP 94.99
1

Analyzing Baseball Data with R Second Edition

Analyzing Baseball Data with R Second Edition

Analyzing Baseball Data with R Second Edition introduces R to sabermetricians baseball enthusiasts and students interested in exploring the richness of baseball data. It equips you with the necessary skills and software tools to perform all the analysis steps from importing the data to transforming them into an appropriate format to visualizing the data via graphs to performing a statistical analysis. The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the ggplot2 graphics functions and employ a tidyverse-friendly workflow throughout. Much of the book illustrates the use of R through popular sabermetrics topics including the Pythagorean formula runs expectancy catcher framing career trajectories simulation of games and seasons patterns of streaky behavior of players and launch angles and exit velocities. All the datasets and R code used in the text are available online. New to the second edition are a systematic adoption of the tidyverse and incorporation of Statcast player tracking data (made available by Baseball Savant). All code from the first edition has been revised according to the principles of the tidyverse. Tidyverse packages including dplyr ggplot2 tidyr purrr and broom are emphasized throughout the book. Two entirely new chapters are made possible by the availability of Statcast data: one explores the notion of catcher framing ability and the other uses launch angle and exit velocity to estimate the probability of a home run. Through the book’s various examples you will learn about modern sabermetrics and how to conduct your own baseball analyses. Max Marchi is a Baseball Analytics Analyst for the Cleveland Indians. He was a regular contributor to The Hardball Times and Baseball Prospectus websites and previously consulted for other MLB clubs. Jim Albert is a Distinguished University Professor of statistics at Bowling Green State University. He has authored or coauthored several books including Curve Ball and Visualizing Baseball and was the editor of the Journal of Quantitative Analysis of Sports. Ben Baumer is an assistant professor of statistical & data sciences at Smith College. Previously a statistical analyst for the New York Mets he is a co-author of The Sabermetric Revolution and Modern Data Science with R.

GBP 52.99
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Computational Genomics with R

Computational Genomics with R

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming to machine learning and statistics to the latest genomic data analysis techniques. The text provides accessible information and explanations always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary it requires different starting points for people with different backgrounds. For example a biologist might skip sections on basic genome biology and start with R programming whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics supervised and unsupervised learning techniques that are important in data modeling and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics such as heatmaps meta-gene plots and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets such as RNA-seq ChIP-seq and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology Max Delbrück Center Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

GBP 42.99
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