AMS/MAA Textbooks : Volume: 53
2011; 205 pp; Softcover
Print ISBN: 978-1-4704-5184-4
Graph Theory presents a natural, reader-friendly way to learn some of the essential ideas of graph theory starting from first principles. The format is similar to the companion text, Combinatorics: A Problem Oriented Approach also by Daniel A. Marcus, in that it combines the features of a textbook with those of a problem workbook. The material is presented through a series of approximately 360 strategically placed problems with connecting text. This is supplemented by 280 additional problems that are intended to be used as homework assignments. Concepts of graph theory are introduced, developed, and reinforced by working through leading questions posed in the problems.
This problem-oriented format is intended to promote active involvement by the reader while always providing clear direction. This approach figures prominently on the presentation of proofs, which become more frequent and elaborate as the book progresses. Arguments are arranged in digestible chunks and always appear along with concrete examples to keep the readers firmly grounded in their motivation.
Spanning tree algorithms, Euler paths, Hamilton paths and cycles, planar graphs, independence and covering, connections and obstructions, and vertex and edge colorings make up the core of the book. Hall's Theorem, the Konig-Egervary Theorem, Dilworth's Theorem and the Hungarian algorithm to the optional assignment problem, matrices, and latin squares are also explored.
Contemporary Mathematics : Volume: 745;
2020; 267 pp; Softcover
MSC: Primary 14;
Print ISBN: 978-1-4704-4898-1
This volume contains the proceedings of the Workshop on Motivic Homotopy Theory and Refined Enumerative Geometry, held from May 14?18, 2018, at the Universitat Duisburg-Essen, Essen, Germany.
It constitutes an accessible yet swift introduction to a new and active area within algebraic geometry, which connects well with classical intersection theory. Combining both lecture notes aimed at the graduate student level and research articles pointing towards the manifold promising applications of this refined approach, it broadly covers refined enumerative algebraic geometry.
Graduate students and research mathematicians interested in enumerative geometry and motivic homology.
The Carus Mathematical Monographs,Volume: 36;
2020; 353 pp; Hardcover
MSC: Primary 05; 11; 20; 51; 52; 91; 94;
Print ISBN: 978-1-4704-5279-7
Combinatorics, or the art and science of counting, is a vibrant and active area of pure mathematical research with many applications. The Unity of Combinatorics succeeds in showing that the many facets of combinatorics are not merely isolated instances of clever tricks but that they have numerous connections and threads weaving them together to form a beautifully patterned tapestry of ideas. Topics include combinatorial designs, combinatorial games, matroids, difference sets, Fibonacci numbers, finite geometries, Pascal's triangle, Penrose tilings, error-correcting codes, and many others. Anyone with an interest in mathematics, professional or recreational, will be sure to find this book both enlightening and enjoyable.
Few mathematicians have been as active in this area as Richard Guy, now in his eighth decade of mathematical productivity. Guy is the author of over 300 papers and twelve books in geometry, number theory, graph theory, and combinatorics. In addition to being a life-long number-theorist and combinatorialist, Guy's co-author, Ezra Brown, is a multi-award-winning expository writer. Together, Guy and Brown have produced a book that, in the spirit of the founding words of the Carus book series, is accessible gnot only to mathematicians but to scientific workers and others with a modest mathematical background.h
Paperback ISBN: 9780128178157
Published Date: 3rd June 2020
Page Count: 600
Mathematical Statistics with Applications in R, Third Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods, such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem-solving in a logical manner. Step-by-step procedure to solve real problems make the topics very accessible.
Presents step-by-step procedures to solve real problems, making each topic more accessible
Provides updated application exercises in each chapter, blending theory and modern methods with the use of R
Includes new chapters on Categorical Data Analysis and Extreme Value Theory with Applications
Wide array coverage of ANOVA, Nonparametric, Bayesian and empirical methods
Readership
Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course
1. Descriptive Statistics
2. Basic Concepts from Probability Theory
3. Additional Topics in Probability
4. Sampling Distributions
5. Statistical Estimation
6. Hypothesis Testing
7. Linear Regression models
8. Design of Experiments
9. Analysis of Variance
10. Bayesian Estimation and Inference
11. Categorical Data Analysis and Goodness of Fit Tests and Applications
12. Nonparametric Tests
13. Empirical Methods
14. Some applications and Some Issues in Statistical Applications: An Overview
Handbook of Statistics, Volume 43
Hardcover ISBN: 9780444642110
Imprint: North Holland
Published Date: 1st June 2020
Page Count: 420
Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more.
Provides the authority and expertise of leading contributors from an international board of authors
Presents the latest release in the Handbook of Statistics series
Updated release includes the latest information on Principles and Methods for Data Science
Readership
Graduate students to senior researchers in statistics and applied mathematicians who wish to refer to very rich and authentic collection in population models and their analytical solutions to their real-world applications. Research scientists and quantitative biologists
1. Competing risks, aims and methods
Ronald Geskus
2. Data analysis and mining of microbial community dynamics
Shinji Nakaoka
3. Support Vector Machines, a robust prediction method with applications in bioinformatics
Arnout Van Messem
4. Data Science - Concepts, Algorithms and Practice
Kalidas Yeturu
5. Bayesian Model Selection for Data with High Dimensions
Naveen Naidu Narisetty
6. High dimensional statistical inference: theoretical development to data analytics
Deepak Ayyala
7. Big data challenges in genomics
Hongyan Xu
8. Analysis of microarray gene expression data using information theory and stochastic algorithm
Narayan Behera
9. Hybrid Models
Arni S.R. Srinivasa Rao
10. Markov Chain Monte Carlo Methods: Theory and Practice
David Spade