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The primary goal of this book is to assist the student to develop the skills necessary to effectively employ the ideas of mathematics to solve military problems. At the simplest level I seek to promote an understanding of why mathematics is useful as a language for characterizing the interaction and relationships among quantifiable concepts, or in mathematical terms, variables. The text explores models of terrorism, attrition, search, detection, missile defense, radar, and operational reliability Throughout the text I emphasize the notion of added value and why it is the driving force behind military mathematical modeling. For a given mathematical model to be deemed a success something must be learned that was not obvious without the modeling procedure. Very often added value comes in the form of a prediction. In the absence of added value the modeling procedure becomes an exercise not unrelated to digging a ditch simply to fill it back up again.
Explore the military and combat applications of modeling and simulation Engineering Principles of Combat Modeling and Distributed Simulation is the first book of its kind to address the three perspectives that simulation engineers must master for successful military and defense related modeling: the operational view (what needs to be modeled); the conceptual view (how to do combat modeling); and the technical view (how to conduct distributed simulation). Through methods from the fields of operations research, computer science, and engineering, readers are guided through the history, current training practices, and modern methodology related to combat modeling and distributed simulation systems. Comprised of contributions from leading international researchers and practitioners, this book provides a comprehensive overview of the engineering principles and state-of-the-art methods needed to address the many facets of combat modeling and distributed simulation and features the following four sections: Foundations introduces relevant topics and recommended practices, providing the needed basis for understanding the challenges associated with combat modeling and distributed simulation. Combat Modeling focuses on the challenges in human, social, cultural, and behavioral modeling such as the core processes of "move, shoot, look, and communicate" within a synthetic environment and also equips readers with the knowledge to fully understand the related concepts and limitations. Distributed Simulation introduces the main challenges of advanced distributed simulation, outlines the basics of validation and verification, and exhibits how these systems can support the operational environment of the warfighter. Advanced Topics highlights new and developing special topic areas, including mathematical applications fo combat modeling; combat modeling with high-level architecture and base object models; and virtual and interactive digital worlds. Featuring practical examples and applications relevant to industrial and government audiences, Engineering Principles of Combat Modeling and Distributed Simulation is an excellent resource for researchers and practitioners in the fields of operations research, military modeling, simulation, and computer science. Extensively classroom tested, the book is also ideal for courses on modeling and simulation; systems engineering; and combat modeling at the graduate level.
Simulation Conceptual Modeling explores several system analysis methods and conceptual modeling techniques. It also discusses appropriate tools that may be used to assist with conceptual modeling. In addition, it discusses how to evaluate the quality of a conceptual model. Some commonly used conceptual modeling techniques and methods include; Data Flow Modeling, Entity Relationship Modeling, Event-Drive Process Chain, Joint Application Development, Place/Transition Net Modeling, State Transition Modeling, Object Role Modeling, and Unified Modeling Language (UML).
This book is about predictive analytics. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this a survey of predictive modeling. A predictive model is a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, one should have a basic understanding of mathematical statistics - it is an advanced book. Some theoretical foundations are laid out but not proven, but references are provided for additional coverage. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. You may download R, from a preferred CRAN mirror at http: //www.r-project.org/. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this a textbook with problem solving in R-but there are no "by-hand" exercises.
I have been behind enemy lines. Once in a city in the middle of East Germany. Berlin was divided by a wall that to cross meant certain death. Now it is one thing to be behind enemy lines, but to live there is another matter. Working behind enemy lines at least brings the hope of returning to friendly territory or overcoming the enemy completely. But the world we live in, even in America, we are smack dead center of enemy territory and the enemy isn't going anywhere, at least not until the Lord comes back and kicks Lucifer's tail. That's right, when your Christian children are at home, school, daycare, or VBS, they are in enemy territory, belonging to Lucifer. But, Israel was in the same boat, especially after Nehemiah returned to rebuild the destroyed vulnerable city of Jerusalem.And they all plotted together to come and fight against Jerusalem and to cause confusion in it. And we prayed to our God and set a guard as a protection against them day and night.Nehemiah 4:7-9Now we set a guard.
Learn how to leverage the power of R for Business Intelligence About This Book Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful. This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R. Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide. Who This Book Is For This book is for data analysts, business analysts, data science professionals or anyone who wants to learn analytic approaches to business problems. Basic familiarity with R is expected. What You Will Learn Extract, clean, and transform data Validate the quality of the data and variables in datasets Learn exploratory data analysis Build regression models Implement popular data-mining algorithms Visualize results using popular graphs Publish the results as a dashboard through Interactive Web Application frameworks In Detail Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence. Style and approach This book will take a step-by-step approach and instruct you in how you can achieve Business Intelligence from scratch using R. We will start with extracting data and then move towards exploring, analyzing, and visualizing it. Eventually, you will learn how to create insightful dashboards that help you make informed decisions—and all of this with the help of real-life examples.
This book is about using open-source tools in data analytics. The book covers several subjects, including descriptive and predictive modeling, gradient boosting, cluster modeling, logistic regression, and artificial neural networks, among other topics.
Crime analysis is both a profession and a set of techniques. The professionals who perform crime analysis, and the techniques they use, are dedicated to helping a police department become more effective through better information. Crime mapping is used by analysts in law enforcement agencies to map, visualize, and analyze crime incident patterns. It is a key component of crime analysis and the CompStat policing strategy. Mapping crime, using Geographic Information Systems (GIS), allows crime analysts to identify crime hot spots, along with other trends and patterns. This book focuses mostly on crime analysis mapping, but includes a discussion of predictive modeling, a special handling of modeling terrorism, and appendices which include a review of probability and statistics and possible board questions. The book is not intended to be all inclusive, nor does it handle the subjects in depth. Rather, it is more of a "survey" of a few crime analysis topics that are dealt with more rigorously by other authors.
Predictive Crime Analysis using R is Dr. Strickland's second crime analysis book. In this volume, rather than using data to describe crime history, he uses it to predict crime using pattern created with advanced clustering methods, crime series linkage, and text analysis. Coverage includes prediction of conventional crime and terrorist attacks. The open-source software R is introduced and used in developing crime data, including Geo-spatial data, and constructing predictive models and performing post analysis. Using actual crime data from cities like Atlanta, Dr. Strickland also shows how to simulate additional data from actual data. Simulated data can then be used in cities with insufficient actual data, but with similar demographics and human behavior.