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Big Data, Big Models, it is a Big Deal

August 13, 2014
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I’ve submitted a poster to the Network on Computational Statistics and Machine Learning (NCSML) workshop “Big Data, Big Models, it is a Big Deal” at the University of Warwick on the 1st & 2nd of September. More details are available from the workshop homepage. My abstract is as follows:

Scalable Bayesian computation for intractable likelihoods in image analysis

The availability of inexpensive, high-quality imaging has given scientists the capacity to generate more data than ever before. In medicine, some patients are scanned daily throughout their course of treatment, to monitor their progress as well as for image-guided therapies. Satellites such as Landsat and MODIS orbit the globe, regularly providing remotely-sensed imagery of the Earth’s surface. Automated methods of image analysis are vital in order to keep pace with the volumes of data that are generated in these settings. Increases in image resolution and sample depth have improved the quality of these images, but this has also resulted in a vast increase in the size of the digital representation. Many methods that were originally developed for much smaller images are infeasible for the image dimensions that are required by current applications. Thus, the scalability of automated methods to meet the needs of real world data is a major concern.

The hidden Potts model is widely applied in image analysis to segment the image pixels and label them according to their underlying classification. The inverse temperature parameter of this model governs the strength of spatial cohesion and therefore has a substantial influence over the resulting model fit. The difficulty arises from the dependence of an intractable normalising constant on the value of the inverse temperature, thus there is no closed form solution for sampling from the distribution directly. We review three computational approaches for addressing this issue, namely pseudolikelihood, path sampling, and the approximate exchange algorithm. We compare the accuracy and scalability of these methods using a simulation study.

This is joint work with Clair Alston and Kerrie Mengersen, Queensland University of Technology, Australia.

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