Inference in Gaussian Process models

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

• Carl Rasmussen, Engineering
• Friday 28 September 2007, 14:00-14:40
• Old Library, Darwin College.

If you have a question about this talk, please contact David MacKay.

Modeling partially unknown functions using examples is a common sub-task in many machine learning applications. Gaussian processes (GPs) are a convenient way to represent and manipulate distributions over functions. In some simple cases exact inference can be done in closed form, but generally approximation methods are required. In this talk I’ll give a brief introduction to GPs and give an overview of approximation techniques and their properties.

This talk is part of the Machine Learning and Inference (One day meeting) series.

This talk is included in these lists:

• Hanchen DaDaDash
• Inference Group Summary
• Interested Talks
• Joint Machine Learning Seminars
• Machine Learning and Inference (One day meeting)
• Old Library, Darwin College
• Required lists for MLG

Note that ex-directory lists are not shown.