Найти книгу: "Bayesian Statistical Modelling"

Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI – Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology

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.

A quantifiable framework for unlocking the unconscious forces that shape markets There has long been a notion that subliminal forces play a great part in causing the seemingly irrational financial bubbles, which conventional economic theory, again and again, fails to explain. However, these forces, sometimes labeled ‘animal spirits’ or ‘irrational exuberance, have remained elusive – until now. The Mystery of Market Movements provides you with a methodology to timely predict and profit from changes in human investment behaviour based on the workings of the collective unconscious. Niklas Hageback draws in on one of psychology's most influential ideas – archetypes – to explain how they form investor’s perceptions and can be predicted and turned into profit. The Mystery of Market Movements provides; A review of the collective unconscious and its archetypes based on Carl Jung’s theories and empirical case studies that highlights and assesses the influences of the collective unconscious on financial bubbles and zeitgeists For the first time being able to objectively measure the impact of archetypal forces on human thoughts and behaviour with a view to provide early warning signals on major turns in the markets. This is done through a step-by-step guide on how to develop a measurement methodology based on an analysis of the language of the unconscious; figurative speech such as metaphors and symbolism, drawn out and deciphered from Big Data sources, allowing for quantification into time series The book is supplemented with an online resource that presents continuously updated bespoken archetypal indexes with predictive capabilities to major financial indexes Investors are often unaware of the real reasons behind their own financial decisions. This book explains why psychological drivers in the collective unconscious dictates not only investment behaviour but also political, cultural and social trends. Understanding these forces allows you to stay ahead of the curve and profit from market tendencies that more traditional methods completely overlook.

Utilise Excel 2013 capabilities to build effective financial models Using Excel for Business Analysis, Revised Edition provides practical guidance for anyone looking to build financial models. Whether for business proposals, opportunity evaluation, financial reports, or any other business finance application, this book shows you how to design, create, and test your model, then present your results effectively using Excel 2013. The book opens with a general guide to financial modelling, with each subsequent chapter building skill upon skill until you have a real, working model of your own. Financial tools, features, and functions are covered in detail from a practical perspective, and put in context with application to real-world examples. Each chapter focuses on a different aspect of Excel modelling, including step-by-step instructions that walk you through each feature, and the companion website provides live model worksheets that give you the real hands-on practice you need to start doing your job faster, more efficiently, and with fewer errors. Financial modelling is an invaluable business tool, and Excel 2013 is capable of supporting the most common and useful models most businesses need. This book shows you how to dig deeper into Excel's functionality to craft effective financial models and provide important information that informs good decision-making. Learn financial modelling techniques and best practice Master the formulas and functions that bring your model to life Apply stress testing and sensitivity analysis with advanced conditionals Present your results effectively, whether graphically, orally, or written A deceptively powerful application, Excel supports many hundreds of tools, features, and functions; Using Excel for Business Analysis eliminates the irrelevant to focus on those that are most useful to business finance users, with detailed guidance toward utilisation and best practice.