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
This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book´s coverage of the latest developments in computational modelling and human phantom development to assess a given technology´s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields.
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.
Written by the Founder and CEO of the prestigious New York School of Finance, this book schools you in the fundamental tools for accurately assessing the soundness of a stock investment. Built around a full-length case study of Wal-Mart, it shows you how to perform an in-depth analysis of that company´s financial standing, walking you through all the steps of developing a sophisticated financial model as done by professional Wall Street analysts. You will construct a full scale financial model and valuation step-by-step as you page through the book. When we ran this analysis in January of 2012, we estimated the stock was undervalued. Since the first run of the analysis, the stock has increased 35 percent. Re-evaluating Wal-Mart 9months later, we will step through the techniques utilized by Wall Street analysts to build models on and properly value business entities. * Step-by-step financial modeling - taught using downloadable Wall Street models, you will construct the model step by step as you page through the book. * Hot keys and explicit Excel instructions aid even the novice excel modeler. * Model built complete with Income Statement, Cash Flow Statement, Balance Sheet, Balance Sheet Balancing Techniques, Depreciation Schedule (complete with accelerating depreciation and deferring taxes), working capital schedule, debt schedule, handling circular references, and automatic debt pay downs. * Illustrative concepts including detailing model flows help aid in conceptual understanding. * Concepts are reiterated and honed, perfect for a novice yet detailed enough for a professional. * Model built direct from Wal-Mart public filings, searching through notes, performing research, and illustrating techniques to formulate projections. * Includes in-depth coverage of valuation techniques commonly used by Wall Street professionals. * Illustrative comparable company analyses - built the right way, direct from historical financials, calculating LTM (Last Twelve Month) data, calendarization, and properly smoothing EBITDA and Net Income. * Precedent transactions analysis - detailing how to extract proper metrics from relevant proxy statements * Discounted cash flow analysis - simplifying and illustrating how a DCF is utilized, how unlevered free cash flow is derived, and the meaning of weighted average cost of capital (WACC) * Step-by-step we will come up with a valuation on Wal-Mart * Chapter end questions, practice models, additional case studies and common interview questions (found in the companion website) help solidify the techniques honed in the book; ideal for universities or business students looking to break into the investment banking field.
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.
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.