Bayesian Computing in Practice (original) (raw)

Software for Bayesian Analysis: Current Status and Additional Needs

AAOSR.Th 8 8-0 479 6&. NAME OF PERFORMING ORGANIZATION GI& OFFICE SYMBOL 7&. NAME OF MONITORING ORGANIZATION Ohio State University OfIlidk Rp.'parch Fotindation IAFOSR/NM 6c, AOORIESS (City, Staff anid ZIP Cc-do) Tol. ADDRESS (City. Staft W, ZIP Code) 1314 Kinnear Road Bldg. 410 Columbus, OH 43212 Bolliig AFB, DC 20332-6448 G&. NAME OF FUNDING/SPONSORING 8b. OFFICE SYMBOL 9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER-AFOR NMAFOSR-84-0 162 Be-ADDRESS (City. State amd ZIP Code) 10. SOURCE Of FUNDING NOS. Bd.40PROGRAM PROJECT TASK WORK UNIT 11. TITLE (Include Secrt C4%a..itiC~tIQ Software for Bayesia, 6.1102F 2304 K 13&. TYPE OF REPORT 13b. TIME COVERED. 23 8 14. DATS OF REPORT (yr.. Mo., Day)

Bayesian Forecasting of Immigration to Selected European Countries by using Expert Knowledge

Journal of the Royal Statistical Society Series A: Statistics in Society, 2010

SummaryThe aim of the paper is to present Bayesian forecasts of immigration for seven European countries to 2025, based on quantitative data and qualitative knowledge elicited from country-specific migration experts in a two-round Delphi survey. In line with earlier results, most of the immigration processes under study were found to be barely predictable in the long run, exhibiting non-stationary features. This outcome was obtained largely irrespectively of the expert knowledge input, which nevertheless was found useful in describing the predictive uncertainty, especially in the short term. It is argued that, under the non-stationarity of migration processes, too long forecasts horizons are inadequate, which is a serious challenge for population forecasts in general.

Some Case Studies Using Bayesian Statistical Models

Cornell University - arXiv, 2021

We provide four case studies that use Bayesian machinery to making inductive reasoning. Our main motivation relies in offering several instances where the Bayesian approach to data analysis is exploited at its best to perform complex tasks, such as description, testing, estimation, and prediction. This work is not meant to be either a reference text or a survey in Bayesian statistical inference. Our goal is simply to provide several examples that use Bayesian methodology to solve data-driven problems. The topics we cover here, include problems in Bayesian nonparametrics, Bayesian analysis of times series, and Bayesian analysis of spatial data.

The BUGS book: A practical introduction to Bayesian analysis

2012

BUGS is a software project that began in Cambridge, England, and has been actively ongoing for more than twenty years. Its core purpose is to provide a computational companion to Bayesian statistical analyses, and the software makes extensive use of Markov chain Monte Carlo and similar simulation methods. At present the software exists in a number of versions, such as WinBUGS, OpenBUGS, and JAGS, which are available free of charge. The authors of The BUGS Book are long-standing contributors to the BUGS project.

Computational Bayesian Statistics

2019

Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

Introduction to Bayesian Econometrics Introduction to Bayesian Econometrics

This concise textbook is an introduction to econometrics from the Bayesian viewpoint. It begins with an explanation of the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It then turns to the definitions of the likelihood function, prior distributions, and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. The Bernoulli distribution is used as a simple example. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions, which leads to an explanation of classical and Markov chain Monte Carlo (MCMC) methods of simulation. The latter is proceeded by a brief introduction to Markov chains. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics, and other applied fields. These include the linear regression model and extensions to Tobit, probit, and logit models; time series models; and models involving endogenous variables.

Bayesian Population Forecasting: Extending the Lee-Carter Method

Demography, 2015

In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.

Bayesian methods

2011

Chapter written for the Handbook of Research Methods and Applications on Empirical Macroeconomics, edited by Nigar Hashimzade and Michael Thornton, forthcoming in 2012 (Edward Elgar Publishing). This chapter presents an introductory review of Bayesian methods for research in empirical macroeconomics