The variational importance sampler is an hybrid algorithm which combines sampling with variational approaches to solve intractable integrals, particularly in the context of Bayesian inference.
Pre-prints of three papers which use or describe the approach are:
Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference by Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass and Stephan Soullier
Bayesian Processing of Microarray Images by Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass and Stephan Soullier
Variational Inference for Visual Tracking by Jaco Vermaak, Neil D. Lawrence and Patrick Pérez (PDF)
This paper gives an brief, non-exhaustive introduction to variational inference:
Variational Inference Guide by Neil D. Lawrence
We have developed software for image processing of cDNA microarrays using the variational importance sampler. The software is automated and provides a higher degree of consistency than other approaches.
MPEG video of Software in action on a small portion of a slide. Purple lines indicate an initial rough grid layout. Blue ovals indicate where the algorithm is looking for the spot. Yellow ovals indicate where it finds it.
Current release is 0.31.
As well as downloading the VIS software you need to obtain the toolboxes specified below.
Page updated on Mon Oct 29 00:18:02 2007