Conditional Counting: Approximate Multinomial Probit Regression

Add to your list(s) Download to your calendar using vCal

• Philipp Hennig (University of Cambridge)
• Monday 24 May 2010, 11:00-12:00
• TCM Seminar Room, Cavendish Laboratory, Department of Physics.

If you have a question about this talk, please contact Emli-Mari Nel.

There are many models for regression on a latent function from discrete data. An interesting case is the probit link function. It tends to only be used for binary classification, because the multi-class case involves an intractable integral.

I will present an approximate inference algorithm for probit inference from multinomial (count) data. It defines an expressive prior on discrete probabilities, allows inference directly from counts (instead of individual discrete variables), and returns an approximate Gaussian posterior. This makes it easy to combine with linear Gaussian and Gaussian process models, providing a predictive model for counts over kernel Hilbert spaces.

This talk is part of the Inference Group series.

This talk is included in these lists:

• All Cavendish Laboratory Seminars
• All Talks (aka the CURE list)
• Biology
• Cambridge Neuroscience Seminars
• Cambridge talks
• Centre for Health Leadership and Enterprise
• Chris Davis' list
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group
• Inference Group Summary
• Interested Talks
• Joint Machine Learning Seminars
• Life Science
• Life Sciences
• ME Seminar
• ML
• Machine Learning Summary
• Neurons, Fake News, DNA and your iPhone: The Mathematics of Information
• Neuroscience
• Neuroscience Seminars
• Neuroscience Seminars
• Required lists for MLG
• School of Physical Sciences
• Stem Cells & Regenerative Medicine
• TCM Seminar Room, Cavendish Laboratory, Department of Physics
• Thin Film Magnetic Talks
• dh539
• rp587
• yk373's list

Note that ex-directory lists are not shown.