Найти книгу: "Applied Bayesian Modelling"

This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBUGS and OPENBUGS. This feature continues in the new edition along with examples using R to broaden appeal and for completeness of coverage.

This book is intended to provide the applied researcher with the capacity to perform statistical analyses with SAS software without wading through pages of technical documentation. The researcher is provided with the necessary SAS statements to run programs for most of the commonly used statistics, explanations of the computer output, interpretations of results, and examples of how to construct tables and write up results for reports and journal articles.

Учебное пособие составлено на основе учебной программы курса английского языка для студентов экономических факультетов, вузов и колледжей и рассчитано на 120 аудиторных часов. Цель пособия – развитие навыков чтения профессионально ориентированных текстов, совершенствование коммуникативных навыков устной и письменной речи на основе текстов по прикладной экономике.

Every serious course contains a textbook and a problem-solving book. This is a solving-problems book after textbook named “Human Being Theory (for Dummies)”. Author analyzes 15 selected films (1994—2016) using concepts of levels and temperaments from the textbook.

Учебник создан с учетом требований федерального государственного образовательного стандарта высшего профессионального образования, а также образовательных стандартов, принятых в НИУ ВШЭ. Материал учебника позволяет не только развивать и совершенствовать языковые и общеакадемические умения и навыки, но и формировать языковую компетентность, лежащую в основе будущей профессиональной деятельности. Для успешного освоения материала необходимо владение английским языком на уровне В2. Для студентов экономических специальностей, изучающих английский язык для профессиональных целей.

A risk measurement and management framework that takes model risk seriously Most financial risk models assume the future will look like the past, but effective risk management depends on identifying fundamental changes in the marketplace as they occur. Bayesian Risk Management details a more flexible approach to risk management, and provides tools to measure financial risk in a dynamic market environment. This book opens discussion about uncertainty in model parameters, model specifications, and model-driven forecasts in a way that standard statistical risk measurement does not. And unlike current machine learning-based methods, the framework presented here allows you to measure risk in a fully-Bayesian setting without losing the structure afforded by parametric risk and asset-pricing models. Recognize the assumptions embodied in classical statistics Quantify model risk along multiple dimensions without backtesting Model time series without assuming stationarity Estimate state-space time series models online with simulation methods Uncover uncertainty in workhorse risk and asset-pricing models Embed Bayesian thinking about risk within a complex organization Ignoring uncertainty in risk modeling creates an illusion of mastery and fosters erroneous decision-making. Firms who ignore the many dimensions of model risk measure too little risk, and end up taking on too much. Bayesian Risk Management provides a roadmap to better risk management through more circumspect measurement, with comprehensive treatment of model uncertainty.