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| Author | : Jeffrey C. Begun |
| Publisher | : |
| Total Pages | : 172 |
| Release | : 2009 |
| Genre | : Atmospheric sulfur dioxide |
| ISBN | : |
| Author | : Richard Michael Cyert |
| Publisher | : Rowman & Littlefield |
| Total Pages | : 234 |
| Release | : 1987 |
| Genre | : Business & Economics |
| ISBN | : 9780847674718 |
No descriptive material is available for this title.
| Author | : Christian Artmann |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 216 |
| Release | : 2009-04-21 |
| Genre | : Mathematics |
| ISBN | : 3540938338 |
Managing uncertainty in new product development projects for improved valuation and decision making is one of the most complex and challenging problems in operations management. It is important for any corporation depending on the success of new products and innovations. This work shows how uncertainty can be handled and partly resolved by conducting an information update during the development process. It is one of the first comprehensive models that combine statistical decision theory in form of Bayesian analysis with a real options framework for projects exposed to different sources of uncertainty. The proposed framework makes an important theoretical contribution in addressing this problem, while at the same time being of significant value to managers who face the difficult task of evaluating and managing complex product development projects.
| Author | : David Kaplan |
| Publisher | : Guilford Publications |
| Total Pages | : 275 |
| Release | : 2023-10-02 |
| Genre | : Social Science |
| ISBN | : 1462553559 |
The second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. It includes new chapters on model uncertainty, Bayesian variable selection and sparsity, and Bayesian workflow for statistical modeling. Clearly explaining frequentist and epistemic probability and prior distributions, the second edition emphasizes use of the open-source RStan software package. The text covers Hamiltonian Monte Carlo, Bayesian linear regression and generalized linear models, model evaluation and comparison, multilevel modeling, models for continuous and categorical latent variables, missing data, and more. Concepts are fully illustrated with worked-through examples from large-scale educational and social science databases, such as the Program for International Student Assessment and the Early Childhood Longitudinal Study. Annotated RStan code appears in screened boxes; the companion website (www.guilford.com/kaplan-materials) provides data sets and code for the book's examples. New to This Edition *Utilizes the R interface to Stan--faster and more stable than previously available Bayesian software--for most of the applications discussed. *Coverage of Hamiltonian MC; Cromwell’s rule; Jeffreys' prior; the LKJ prior for correlation matrices; model evaluation and model comparison, with a critique of the Bayesian information criterion; variational Bayes as an alternative to Markov chain Monte Carlo (MCMC) sampling; and other new topics. *Chapters on Bayesian variable selection and sparsity, model uncertainty and model averaging, and Bayesian workflow for statistical modeling.
| Author | : Paul George Benson |
| Publisher | : |
| Total Pages | : 152 |
| Release | : 1977 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
| Author | : Nguyen Ngoc Thach |
| Publisher | : Springer Nature |
| Total Pages | : 865 |
| Release | : 2022-05-28 |
| Genre | : Technology & Engineering |
| ISBN | : 3030986896 |
This book overviews latest ideas and developments in financial econometrics, with an emphasis on how to best use prior knowledge (e.g., Bayesian way) and how to best use successful data processing techniques from other application areas (e.g., from quantum physics). The book also covers applications to economy-related phenomena ranging from traditionally analyzed phenomena such as manufacturing, food industry, and taxes, to newer-to-analyze phenomena such as cryptocurrencies, influencer marketing, COVID-19 pandemic, financial fraud detection, corruption, and shadow economy. This book will inspire practitioners to learn how to apply state-of-the-art Bayesian, quantum, and related techniques to economic and financial problems and inspire researchers to further improve the existing techniques and come up with new techniques for studying economic and financial phenomena. The book will also be of interest to students interested in latest ideas and results.
| Author | : Marco A.R. Ferreira |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 243 |
| Release | : 2007-07-27 |
| Genre | : Business & Economics |
| ISBN | : 0387708979 |
This highly useful book contains methodology for the analysis of data that arise from multiscale processes. It brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for full accounting of uncertainty, and also addresses the delicate issue of uncertainty at multiple scales. These methods can handle different amounts of prior knowledge at different scales, as often occurs in practice.
| Author | : Andrew Gelman |
| Publisher | : CRC Press |
| Total Pages | : 677 |
| Release | : 2013-11-01 |
| Genre | : Mathematics |
| ISBN | : 1439840954 |
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
| Author | : Ralph C. Smith |
| Publisher | : SIAM |
| Total Pages | : 400 |
| Release | : 2013-12-02 |
| Genre | : Computers |
| ISBN | : 1611973228 |
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers can find data used in the exercises and other supplementary material.
| Author | : Osvaldo Martin |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 350 |
| Release | : 2018-12-26 |
| Genre | : Computers |
| ISBN | : 1789349664 |
Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key FeaturesA step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZA modern, practical and computational approach to Bayesian statistical modelingA tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learnBuild probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical modelsFind out how different models can be used to answer different data analysis questionsCompare models and choose between alternative onesDiscover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian frameworkWho this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.
