JiscMail Logo
Email discussion lists for the UK Education and Research communities

Help for ALLSTAT Archives


ALLSTAT Archives

ALLSTAT Archives


allstat@JISCMAIL.AC.UK


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

ALLSTAT Home

ALLSTAT Home

ALLSTAT  October 2013

ALLSTAT October 2013

Options

Subscribe or Unsubscribe

Subscribe or Unsubscribe

Log In

Log In

Get Password

Get Password

Subject:

Professor David Draper presents Bayesian Courses at SSC, University of Reading, UK November 2013

From:

Emma Hollands <[log in to unmask]>

Reply-To:

Emma Hollands <[log in to unmask]>

Date:

Tue, 8 Oct 2013 10:21:26 +0000

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (174 lines)

Greetings, and apologies for cross-posting...

We are announcing the following courses, which are scheduled to take place at the Statistical Services Centre in November 2013. 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]

****************************************************************************
Day 1: Bayesian Modeling, Inference and Prediction; 27 November 2013
****************************************************************************
Day 2: Bayesian Hierarchical Modeling; 28 November 2013
****************************************************************************
Day 3: Bayesian Model Specification; 29 November 2013
****************************************************************************

Prices: 370 for any one day; 695 for any two days; 995 for all three days.
A 30% academic discount is available for these courses. [Terms and conditions apply.]  Note a special discount of 50% will be given to students for these specific courses. Please indicate you wish to apply for a discount when you register, together with information supporting your eligibility.  [Terms and conditions apply.]

Please view http://www.reading.ac.uk/ssc/n/Short%20Courses/bayesianmodelling.htm for more details.  Further information is also given below.
**********************************************************************************************************************

Course outline
**************
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 the third day, which is an intermediate-level course on Bayesian model specification,

* or any two days, or all three days.

Day 1 of this course will:
*********************
(1) Compare and contrast the frequentist and Bayesian conceptions of 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) Introduce Bayesian hierarchical modeling via meta-analysis, the study of how information can be combined across experiments to provide a better summary than those obtained by examining one experiment at a time;

(2) Discuss the critical role played by the choice of prior distributions in Bayesian hierarchical models;

(3) Illustrate the use of latent variables (random effects) as an approach to describing unexplained heterogeneity; and

(4) Explore two in-depth case studies involving random-effects Poisson regression and mixed-effects logistic regression.

Day 3 of this course will:
*********************
(1) Provide an overview of the process of Bayesian model specification;

(2) Introduce five basic principles -- the Calibration Principle, the Modeling-As-Decision Principle, the Prediction Principle, the Inference-Versus-Decision Principle and Cromwell's Rule (Parts 1 and 2) -- and show you how they inform good Bayesian model building and model criticism; and

(3) Introduce you to Calibration Cross-Validation, Bayes factors, BIC, DIC and log scoring (in WinBUGS and R) as methods for finding good Bayesian models.

All three days of the course will be based on a series of practical real-world case studies.

***********************************************************************************************************************

Who should attend?
******************
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.

The first day of the course will assume no previous exposure to Bayesian ideas or methods. Participants interested in entering the course on the second or third day should ideally have had exposure to the ideas on the days preceding their entry day.

***********************************************************************************************************************

How you will benefit
*******************
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
***************
Day 1: Bayesian Modeling, Inference and Prediction
******************************************
* Background and basics: strengths and weaknesses of the classical, frequentist and Bayesian probability paradigms
* Sequential learning via Bayes' 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 Hierarchical Modeling
********************************
* 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 modeling
* 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.

Day 3: Bayesian Model Specification
******************************
* The big picture in Bayesian model specification
* The Calibration Principle, the Modeling-As-Decision Principle, the Prediction Principle, the Inference-Versus-Decision Principle, and Cromwell's Rule (Parts 1 and 2)
* 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
* A generic Bayesian model-search algorithm
* False positive/false negative trade-offs in comparing {Bayes factors, BIC} and {DIC, log scores} on their ability to correctly discriminate between models.

**********************************************************************************************************************

Guest Presenter
****************
David Draper is a Professor of Statistics in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz (USA); in the period 1 July -31 December 2013 he is also a Distinguished Statistical Scientist and Visiting Professor at eBay Research Labs in San Jose, CA.

He is a Fellow of the American Association for the Advancement of Science, the American Statistical Association (ASA), the Institute of Mathematical Statistics, 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 about 140 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 about 1,150 times, and taken together his publications have been cited more than 9,200 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, environmental risk assessment and eCommerce.

His short courses have received Excellence in Continuing Education Awards from the American Statistical Association on two occasions, corresponding to days 1 and 2 of this course.

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
****************
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
*********
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 view: http://www.reading.ac.uk/ssc/n/visiting.htm.

Emma Hollands
Training Co-ordinator
Statistical Services Centre
University of Reading
Harry Pitt Building
Whiteknights Road
Reading RG6 6FN
UK

e-mail: [log in to unmask]
Tel: +44(0)118 378 8689
Fax: +44(0)118 378 8458
www.reading.ac.uk/ssc

You may leave the list at any time by sending the command

SIGNOFF allstat

to [log in to unmask], leaving the subject line blank.

Top of Message | Previous Page | Permalink

JiscMail Tools


RSS Feeds and Sharing


Advanced Options


Archives

July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
2006
2005
2004
2003
2002
2001
2000
1999
1998


JiscMail is a Jisc service.

View our service policies at https://www.jiscmail.ac.uk/policyandsecurity/ and Jisc's privacy policy at https://www.jisc.ac.uk/website/privacy-notice

Secured by F-Secure Anti-Virus CataList Email List Search Powered by the LISTSERV Email List Manager