Introduction to Bayesian inference

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

• Thomas Brouwer (University of Cambridge)
• Tuesday 17 November 2015, 13:00-14:00
• Computer Laboratory, William Gates Building, Room SW01.

If you have a question about this talk, please contact Heidi Howard.

Probabilistic models allow us to make flexible and robust systems that handle uncertainty in our data gracefully. In a Bayesian approach we express prior beliefs over our model’s parameters, and update our beliefs by finding the posterior distribution over the parameters. In this lecture, we will consider how these models can be described graphically, and how efficient Bayesian inference can be used for training.

This talk is part of the Research Students Lecture Series series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge talks
• Computer Education Research
• Computer Laboratory, William Gates Building, Room SW01
• Computing Education Research
• Department of Computer Science and Technology talks and seminars
• Guy Emerson's list
• Interested Talks
• School of Technology
• Trust & Technology Initiative - interesting events
• bld31

Note that ex-directory lists are not shown.