A comprehensive account of the theory and application of Monte Carlo methods Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems. Written by authorities in the field, the book places emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. Focusing on the concepts and application of Monte Carlo techniques, Fast Sequential Monte Carlo Methods for Counting and Optimization includes: <ul>
<li>Detailed algorithms needed to practice solving real-world problems</li> <li>Numerous examples with Monte Carlo method produced solutions within the 1-2% limit of relative error</li> <li>A new generic sequential importance sampling algorithm alongside extensive numerical results</li> <li>An appendix focused on review material to provide additional background information</li> </ul>
Fast Sequential Monte Carlo Methods for Counting and Optimization is an excellent resource for engineers, computer scientists, mathematicians, statisticians, and readers interested in efficient simulation techniques. The book is also useful for upper-undergraduate and graduate-level courses on Monte Carlo methods.

Fast Sequential Monte Carlo Methods for Counting and Optimization (Wiley Series in Probability and Statistics)

Computers & Technology

Vaisman, Radislav

Rubinstein, Reuven Y.

Ridder, Ad

Computer Science

wiley

"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey!" -Nicholas Hoell, University of Toronto
"This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche. -Adam Loy, Carleton College The purpose of Data Science and Machine Learning: Mathematical and Statistical Methods is to provide an accessible, yet comprehensive textbook intended for students interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science. Key Features: <ul>
<li>Focuses on mathematical understanding.</li> <li>Presentation is self-contained, accessible, and comprehensive.</li> <li>Extensive list of exercises and worked-out examples.</li> <li>Many concrete algorithms with Python code.</li> <li>Full color throughout.</li> </ul>
The Authors: Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics at The University of Queensland. He has published over 120 articles and five books in a wide range of areas in mathematics, statistics, data science, machine learning, and Monte Carlo methods. He is a pioneer of the well-known Cross-Entropy method―an adaptive Monte Carlo technique, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance. 
Zdravko Botev, PhD, is an Australian Mathematical Science Institute Lecturer in Data Science and Machine Learning with an appointment at the University of New South Wales in Sydney, Australia. He is the recipient of the 2018 Christopher Heyde Medal of the Australian Academy of Science for distinguished research in the Mathematical Sciences.
Thomas Taimre, PhD, is a Senior Lecturer of Mathematics and Statistics at The University of Queensland. His research interests range from applied probability and Monte Carlo methods to applied physics and the remarkably universal self-mixing effect in lasers. He has published over 100 articles, holds a patent, and is the coauthor of Handbook of Monte Carlo Methods (Wiley). Radislav Vaisman, PhD, is a Lecturer of Mathematics and Statistics at The University of Queensland. His research interests lie at the intersection of applied probability, machine learning, and computer science. He has published over 20 articles and two books.

Data Science and Machine Learning: Mathematical and Statistical Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Business & Money

Kroese, Dirk P.

Taimre, Thomas

Botev, Zdravko

Economics

Chapman and Hall/CRC

Ternary means “based on three”. This book deals with reliability investigations of networks whose components subject to failures can be in three states –up, down and middle (mid), contrary to traditionally considered networks having only binary (up/down) components. Extending binary case to ternary allows to consider more realistic and flexible models for communication, flow and supply networks

Ternary Networks: Reliability and Monte Carlo (SpringerBriefs in Electrical and Computer Engineering)

Gertsbakh, Ilya

Shpungin, Yoseph

Springer

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