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ALLSTAT  August 2011

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Subject:

Bayes on Beach 2011 Australia

From:

Dow Jaemjamrat <[log in to unmask]>

Reply-To:

Dow Jaemjamrat <[log in to unmask]>

Date:

Thu, 18 Aug 2011 15:55:45 +1000

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Dear Allstat

Bayes on the Beach 2011

6th & 7th October

Vibe Hotel* Surfers Paradise

The conference will provide a forum for discussion on developments and applications of Bayesian statistics. The International keynote speaker is Prof. Sudipto Banerjee from the University of Minnesota and the Australian keynote speaker is Assistant Professor. Edward Cripps from the University of Western Australia. The format includes seminars, contributed sessions, a poster session, tutorials and workshops.

Registration will open on the 1st September 2011.

Students Registration fee                        $200.00

Regular Registration       fee                   $400.00



Please contact Vibe Hotel Gold Coast to book accommodation

http://www.vibehotels.com.au/default.asp?page=/vibe-locations/gold-coast-hotels/vibe-hotel-gold-coast

Call for abstract for Poster session and Contributed session closes Thursday 15th September. 200 words abstract only and please indicate if you wish the abstract to be considered for a poster or a contributed talk.

All abstracts should be sent to Dow [log in to unmask]<mailto:[log in to unmask]>



In addition to the conference there will be a one day workshop by Sudipto Banerjee held at QUT.

Introduction to Hierarchical Modelling for Spatial Data
What: Workshop by Prof. Sudipto Banerjee on
When: Wednesday 5th October
Where: Queensland University of Technology

Registration:

            Students                                    $80.00

            Conference attendees                 $150.00

            Workshop Only                          $300.00



Key Note Speakers Abstracts

Presenter: Professor Sudipto Banerjee
Title: Computationally feasible hierarchical modelling strategies for large spatial datasets
Abstract:
Large point referenced datasets are common in the environmental and natural sciences. The computational burden in fitting large spatial datasets undermines estimation of Bayesian models. We explore several improvements in low-rank and other scalable spatial process models including reduction of biases and process-based modelling of ``centres'' or ``knots'' that determine optimal subspaces for data projection. We also consider alternate strategies for handling massive spatial datasets. One approach concerns developing process-based super-population models and developing Bayesian finite-population sampling techniques for spatial data. We also explore model-based simultaneous dimension-reduction in space, time and the number of variables. Flexible and rich hierarchical modelling applications in forestry are demonstrated.

Presenter: Assistant Professor Edward Cripps
Title Mixture of random effects for individual's learning behaviour
Abstract:
We present a dynamic mixture of random effects model applied to a current topic in the
psychology literature. In psychology, the implicit theory of abilities proposes that individuals are classified as one of two groups:entity theorists who believe ability is innate and incremental theorists who believe ability is anaquired set of skills. Entity theorists are more likely to interpret failure as evidenceof a lack of ability and doubt their future capacity to learn the task. Incremental theorists are more likely to interpret failures as part of a learning strategy, potentially leading to recovery over time. The hypothesis is that learning performances of entity theorists are more prone to downward ``spirals'' than incremental theorists. The Accelerated Learning Laboratory, Melbourne Business School, has conducted experiments in which individuals from both groups were subjected to repeated tasks and their performances evaluated.  To assess the hypothesis we model the performance of an individual as a function of that individual's personality self-classification using a time-varying mixture of potentially two constrained random effects models, one before spiralling behaviour begins and another after.  Performance is modelled dynamically by allowing for the commencement of the spiral to be a function of time and to vary with individuals. So for each individual we average over another class of models which are the possible locations of the spiral, and performance is predicted by weighting all possible models by their posterior probability.

Please contact Dow [log in to unmask]<mailto:[log in to unmask]> for any further information +


(Please note that my days of work have changed to Tuesdays and Thursdays)
Dow Jaemjamrat | Administrative Assistant | QUT |Faculty of Science & Technology
Ph: +61 7 3138 2063 |Mob: +61423 873 774|Email: [log in to unmask] /

CRICOS Provider Code: 00213J. This email is intended solely for the addressee. It may contain private or confidential information. If you are not the intended addressee, you must take no action based on it, nor show a copy to anyone. Kindly notify the sender by reply email. Opinions and information in this email which do not relate to the official business of the Queensland University of Technology shall be understood as neither given nor endorsed by the University.


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