Introduction to Bayesian Modelling and Analysis – Melbourne

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This event is now closed for registrations.

Professor Kerrie Mengersen and the Victorian Branch of the Statistical Society are pleased to present the workshop “Introduction to Bayesian Modelling and Analysis” in Melbourne on 29-30th March.

 

About the Presenter

Professor Mengersen is a Professor of Statistics in the School of Mathematical Sciences and Institute for Future Environments at QUT. She has around 25 years of experience in statistical modelling, analysis and computation, with particular focus on applications in health, environment and industry. Her expertise includes analysis and integration of complex datasets, encapsulation and effective use of expert information, spatio-temporal analysis and complex systems modelling. In addition to academic outputs comprising over 250 journal articles, she has a continuous record of commercial consultancies with selected relevant clients including Corrs Chambers Westgarth (risk), Goronickel (design), Qld Environmental Protection Agency and Healthy Waterways (water quality), Port of Brisbane (prediction), Qld Dept Natural Resources (environmental statistical modelling and analysis), Western Mining Company (analysis) and Dairy Australia (triple bottom line sustainability). Professor Mengersen is a Deputy Director of the ARC Centre of Excellence in Mathematical and Statistical Frontiers for Big Data, Big Models and New Insights (ACEMS), and an ARC Laureate Fellow.

 

About the course

Overview

Bayesian modelling and data analysis are becoming a standard part of the statistical toolkit. Its appeal includes the availability of hierarchical models for better describing complex systems, the use of priors to describe uncertainty and include external information in the analysis, and the direct probabilistic interpretation of the results.

While simple Bayesian models can be analysed analytically, most analysis is via Monte Carlo methods such as Markov chain Monte Carlo (MCMC). There is a great range of MCMC and other algorithms available now for Bayesian computation.

This two-day course introduces the practising statistician to Bayesian analysis. The course is strongly practical, with emphasis on understanding the fundamental concepts, modelling in a Bayesian context, using MCMC and ‘doing’ Bayesian analysis via the software packages R and WinBugs.

Please note that this course is introductory. It assumes some knowledge of statistics but no knowledge of Bayesian or MCMC approaches.

 

IT Requirements

Participants are requested to bring a laptop with the following software loaded:

– R (freeware statistics program)

– WinBugs (freeware program)

 

Course Outline

The following topics will be covered:

  • What is Bayesian Statistics
  • Priors, models & results
  • Common MCMC algorithms
  • Model fit & model choice
  • Role & formulation of priors
  • Examples of different types of models
  • Reporting of Bayesian analysis results, with examples from published literature

 

Tentative Course Timetable (subject to change)

Day 1

9.00   – 10.30am                    Overview of Bayesian Modelling

10.30 – 10:50am                    Morning tea

10:50 – 12.30pm                    Overview of MCMC

12.30 – 1.30pm                     BYO Lunch

1.30   –  3.10pm                     Bayesian generalized linear models and Practical Session 1

3.10   –  3.30pm                    Afternoon tea

3.30   –  5.00pm                    Practical Session 2

 

Day 2

9.00   – 10.30am                    Bayesian latent variable modelling

10.30 – 10:50am                    Morning tea

10:50 – 12.30pm                    Practical session 3

12.30 – 1.30pm                     BYO Lunch

1.30   –  3.10pm                     Algorithms: MCMC and more

3.10   –  3.30pm                     Afternoon tea

3.30   –  5.00pm                     Writing up a Bayesian analysis


 

Target Audience

Practising statisticians wanting to learn and apply the fundamentals of Bayesian analysis or researchers in other disciplines with a statistical knowledge equivalent to one year of undergraduate study.

Basic statistical knowledge, and statistical computing, but no knowledge of Bayesian methods.

 

Learning Objectives

Attendees will gain a basic understanding the fundamental concepts, modelling in a Bayesian context, using MCMC and ‘doing’ Bayesian analysis via the software packages R and WinBugs. Participants will be introduced to a range of models for describing complex data and the application of these models to real problems.

 

Course Costs:

SSA Members – $480

SSA Student Members** – $300

Non-SSA Members – $600

Non-SSA Student Members** – $330

Bookings are now open  and close strictly on Wednesday, 23 March 2016. Any bookings accepted after this date -should places still be available- will incur an additional $100 late-booking-fee.

** Proof of Valid University ID required

 

Travel Expenses

Occasionally workshops have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen. Please contact the SSA Office before making any travel arrangements to confirm that the workshop will go ahead, because the SSAI will not be held responsible for any travel or accommodation expenses incurred due to a workshop cancellation.

 

Cancellation Policy

Cancellations received prior to Wednesday,  23 March 2016 will be refunded, minus a $20 administration fee.

From 23 March 2016 no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to [email protected].

Introduction to Bayesian Modelling and Analysis - Melbourne
When: 29/03/2016 - 30/03/2016
Time: 9:00 am - 5:00 pm
Cost: from $300.00 (students)
Location: RMIT Access Grid Room, Building 8, Level 9,
360 Swanston Street,
Melbourne,
VIC 3000
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