Detailed Modeling and Simulation of Solar Water Heater: Ashish Agarwal, R. M. Sarviya
Autodesk 3ds Max 2020: A Detailed Guide to Modeling, Texturing, Lighting, and Rendering: Pradeep Mamgain
Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.
Directly oriented towards real practical application, this book develops both the basic theoretical framework of extreme value models and the statistical inferential techniques for using these models in practice. Intended for statisticians and non-statisticians alike, the theoretical treatment is elementary, with heuristics often replacing detailed mathematical proof. Most aspects of extreme modeling techniques are covered, including historical techniques (still widely used) and contemporary techniques based on point process models. A wide range of worked examples, using genuine datasets, illustrate the various modeling procedures and a concluding chapter provides a brief introduction to a number of more advanced topics, including Bayesian inference and spatial extremes. All the computations are carried out using S-PLUS, and the corresponding datasets and functions are available via the Internet for readers to recreate examples for themselves. An essential reference for students and researchers in statistics and disciplines such as engineering, finance and environmental science, this book will also appeal to practitioners looking for practical help in solving real problems. Stuart Coles is Reader in Statistics at the University of Bristol, UK, having previously lectured at the universities of Nottingham and Lancaster. In 1992 he was the first recipient of the Royal Statistical Society´s research prize. He has published widely in the statistical literature, principally in the area of extreme value modeling.
A comprehensive and hands-on introduction to the core concepts, methods, and applications of agent-based modeling, including detailed NetLogo examples.
Modeling of Reinforced Concrete Structures:Detailed Three-Dimensional Nonlinear Hybrid Simulation for the Analysis of Large-Scale Reinforced Concrete Structures George Markou
An extensive guide to help you analyze data more effectively. Learn more about how to analyze data now! Explore the field of data science and the way to analyze big and small data. This elaborate guide will take you on a journey to multiple aspects of this skill. There is a trick, a science, to doing it the right way, and some of the most important secrets will be revealed in the chapters ahead of you. Dive into the complicated matter of analyzing and mining for data correctly. Forget about intuition or assumptions. You’ll learn, among others:Linear, probabilistic, and other models to use in the visualization and analysis of data you have found.Systems such as clustering, viewing genetic algorithms, and neural methods.Assessment analysis strategies, organization, and numeric predictions.Modeling data and imagining.The three Vs of big data and what to do with them.Software recommendations and applications.What to do exactly with big data.Basics, risks, and tactics to analyze data.Social network data analysis.Purposes for health care, business, and industrial data.Tips on analyzing decision trees, regression, and sentiment.Attributes, classifications, data sets, and kinds of learning you must recognize to fully be aware of that with which you are dealing.Data quality and data quantity thoughts.Data-mining procedure steps, including CRISP-DM and SEMMA.Machine algorithms and interesting sidenotes regarding them.Instructions, infrastructure, edition, and other methods.Perception and cognition basics that apply to data.Effectual uses of regression, database querying, machine learning, and data warehousing.Data creates truths you can trust in if you draw the right conclusions. Drawing those conclusions involves clear skills and a background in information that leads to t 1. Language: English. Narrator: Ryan Simpson. Audio sample: http://samples.audible.de/bk/acx0/134442/bk_acx0_134442_sample.mp3. Digital audiobook in aax.
This book offers a complete basic course in Fully Communication Oriented Information Modeling (FCO-IM), a Fact Oriented Modeling (FOM) data modeling technique. The book is suitable for self-study by beginner FCO-IM modelers, whether or not experienced in other modeling techniques. An elaborate case study is used as illustration throughout the book. The book also illustrates how data models in other techniques can be derived from an elementary FCO-IM model. The context of fact oriented modeling is given as well, and perspectives on information modeling indicate related areas of application and further reading. Fact Oriented Modeling methods (like FCO-IM) have three major advantages over other data modeling techniques: FCO-IM captures business semantics. The meaning of facts is captured by incorporating into the model expressions of concrete facts in clear sentences, which are understood by both domain experts and information modelers. FCO-IM includes a detailed working procedure that tells you exactly how to make a data model. Many techniques are clear about what is to be modeled, but few offer a detailed set of guidelines and checks that tell you how to draw up, check and validate your model. FCO-IM focuses on elementary facts, avoiding premature clustering of facts (in entities) but also avoiding considering only incomplete fragments of facts (attributes). From an elementary model, data models in other techniques can be automatically derived (ERM, UML, Data Vault, Star Schema, and Relational and NoSQL databases).
Polymodeling is a method used to construct highly detailed, computer generated 3d models. The artist is given the freedom to determine the flow and overall mass of their creation through this free-form digital sculpting method. This book is a collection of tips, tricks and techniques on how to create professional models for advertising on T.V and the web. The author has tons of industry experience using Max toward this end, and he shares the secrets of his trade. As Production Modeler for some of today´s hottest studios (including GuerillaFx, Coke Zero, MTV, Old Navy, Nike, Target, HP) Todd Daniele brings real-world experience to the book. Daniele teaches the technical aspects of polymodeling, while showing how to ultimately create content in a dynamic, efficient manner. Associated web site offers instructional files that show the models in progressive stages of development; plus a supporting internet forum: readers can log-on to this forum to ask questions or comment on anything covered in the book.