Greetings, and apologies for cross-posting.
The Statistical Services Centre at the University of Reading is pleased to announce the following new courses, which are scheduled to take place at the Centre in Reading in November 2012. Summary information is given below. For registration forms please see http://www.reading.ac.uk/ssc/ providing your address and/or fax number, or email [log in to unmask]
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Day 1: Bayesian Modeling, Inference and Prediction; 22 November 2012
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Day 2: Bayesian Model Comparison and Hierarchical Modeling; 23 November 2012
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Price: 370 GBP for any one day; 695 GBP for days 1 and 2 taken together
Discounts: An Academic discount is available for these courses. Terms and conditions apply
Please view:
http://www.reading.ac.uk/ssc/n/Short%20Courses/bayesianmodelling.htm
for more details.
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Course outline
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This is a short course that offers you flexibility: you can either take:-
* the first day, which is an introductory course on Bayesian modeling, inference and prediction,
* or the second day, which is an intermediate-level course on Bayesian model comparison and hierarchical modeling,
* or both days, for a discounted price in relation to twice the one-day fee.
Bayesian methods offer an approach to inference, prediction and decision-making that allows you to synthesize all relevant sources of information in drawing conclusions and making decisions in the presence of uncertainty: you can bring together information both internal to, and external to, your current data set to create a coherent summary of all that's known about the problem you're studying.
Day 1 of this course will:
(1) compare and contrast the frequentist and Bayesian approaches to probability, highlighting the strengths and weaknesses of both;
(2) review maximum-likelihood fitting of statistical models;
(3) show you how to obtain Bayesian solutions to inferential and predictive problems analytically and in closed form (when such solutions are available); and
(4) introduce you to simulation-based Bayesian model-fitting using Markov-chain Monte Carlo (MCMC) methods, in the freeware packages WinBUGS and R, when closed-form solutions are not possible.
Day 2 of this course will:
(1) provide an overview of the process of Bayesian model specification;
(2) introduce four basic principles -- the Calibration Principle, the Modeling-As-Decision Principle, the Prediction Principle, and the Inference-Versus-Decision Principle -- and show you how they inform good Bayesian model building and model criticism;
(3) introduce you to Calibration Cross-Validation, Bayes factors, BIC, DIC and log scoring as methods for finding good Bayesian models; and
(4) cover introductory and intermediate-level topics in Bayesian hierarchical modeling, including the fitting of Bayesian random-effects and mixed models in WinBUGS and R.
Both days of the course will be based on a series of practical real-world case studies, and the first day of the course will assume no previous exposure to Bayesian ideas or methods.
Who should attend?
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Statisticians, biostatisticians, epidemiologists, data analysts, data-miners, and machine-learning specialists who wish to broaden and deepen
(a) their understanding of Bayesian methods and (b) their toolkits for using Bayesian models to find meaningful patterns, arrive at statistically sound inferences and make better decisions.
Some graduate coursework in statistics (or an allied field such as biostatistics, epidemiology or machine learning) will provide sufficient mathematical background for participants. To get the most out of the course, participants should be comfortable with hearing the course presenter discuss
(a) differentiation and integration of functions of several variables and
(b) discrete and continuous probability distributions (joint, marginal, and conditional) for several variables at a time, but all necessary concepts will be approached in a sufficiently intuitive manner that rustiness on these topics will not prevent understanding of the key ideas.
How you will benefit
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You will:
(1) gain a deeper understanding of maximum-likelihood-based methods and when they can be expected to behave in a sub-optimal manner;
(2) broaden and deepen your facility in the fitting and interpretation of Bayesian models to solve important problems in science, public policy and business; and
(3) learn how to write your own programs in WinBUGS and R to fit Bayesian models in your own work.
Course content
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Day 1: Bayesian Modeling, Inference and Prediction
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* Background and basics: strengths and weaknesses of the classical, frequentist and Bayesian probability paradigms
* Sequential learning via Bayes's Theorem
* Coherence as a form of internal calibration
* Bayesian decision theory via maximization of expected utility
* Review of frequentist modeling and maximum-likelihood inference
* Exchangeability as a Bayesian concept parallel to frequentist independence
* Prior, posterior, and predictive distributions
* Bayesian conjugate analysis of binary outcomes, and comparison with frequentist modeling
* Conjugate analysis of integer-valued outcomes (Poisson modeling)
* Conjugate analysis of continuous outcomes (Gaussian modeling)
* Multivariate unknowns and marginal posterior distributions
* Introduction to simulation-based computation, including rejection sampling and Markov chain Monte Carlo (MCMC) methods
* MCMC implementation strategies.
Day 2: Bayesian Model Comparison and Hierarchical Modeling
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* The big picture in Bayesian model specification
* The Calibration Principle, the Modeling-As-Decision Principle, the Prediction Principle, and the Inference-Versus-Decision Principle
* Model expansion as a tool for improving Bayesian modeling: embedding a deficient model in a larger class of models of which it's a special case
* Methods for finding good Bayesian models: Calibration Cross Validation, Bayes factors, BIC, DIC and log scores
* False positive/false negative trade-offs in comparing {Bayes factors, BIC} and {DIC, log scores} on their ability to correctly discriminate between
models
* Bayesian hierarchical modeling
* Hierarchical modeling with latent variables as an approach to mixture modeling
* Bayesian fixed- and random-effects meta-analysis
* Prior distributions in Bayesian hierarchical modelling
* Bayesian fitting of random-effects and mixed models
* Comparison of likelihood-based and Bayesian methods for fitting hierarchical models: circumstances in which likelihood-based fitting can be poorly calibrated.
Guest Presenter
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David Draper is a Professor of Statistics in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz (USA).
He is a Fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and the Royal Statistical Society; from 2001 to 2003 he served as the President-Elect, President, and Past President of the International Society for Bayesian Analysis (ISBA).
He is the author or co-author of more than 100 contributions to the methodological and applied statistical literature, including articles in the Journal of the Royal Statistical Society (Series A, B and C), the Journal of the American Statistical Association, the Annals of Applied Statistics, Bayesian Analysis, Statistical Science, the New England Journal of Medicine, and the Journal of the American Medical Association; his 1995 JRSS-B article on assessment and propagation of model uncertainty has been cited more than
1,050 times.
His research is in the areas of Bayesian inference and prediction, model uncertainty and empirical model-building, hierarchical modeling, Markov Chain Monte Carlo methods, and Bayesian nonparametric methods, with applications mainly in medicine, health policy, education, and environmental risk assessment.
His short courses have received Excellence in Continuing Education Awards from the American Statistical Association on two occasions.
He has won or been nominated for major teaching awards everywhere he has taught (the University of Chicago; the RAND Graduate School of Public Policy Studies; the University of California, Los Angeles; the University of Bath (UK); and the University of California, Santa Cruz).
He has a particular interest in the exposition of complex statistical methods and ideas in the context of real-world applications.
Course Materials
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Please note that there will not be any printed notes for this course; a web link to the materials (PDF files with course notes and .txt files containing data sets and R and WinBUGS code) will be provided to registered participants several weeks before the course occurs.
Location
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The Statistical Services Centre at Whiteknights campus, University of Reading, is in a prime location in the South-East of England and has excellent transport links. The University is close to the M4 motorway, allowing easy access by car. Reading's railway station has high speed links to and from London Paddington, as well as regular services to and from other cities around the UK. There are direct services to and from both London Heathrow and London Gatwick Airports. For further details please view: http://www.reading.ac.uk/ssc/n/visiting.htm
Kind Regards
Emma Hollands
Training Co-ordinator
Statistical Services Centre,
University of Reading,
Harry Pitt Building,
Whiteknights Road,
Reading, RG6 6FN, UK
Tel: +44(0)118 378 8689
www.reading.ac.uk/ssc
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