COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Computer Laboratory Research Students' Lectures 2014 > Structure learning in Bayesian Networks

## Structure learning in Bayesian NetworksAdd to your list(s) Download to your calendar using vCal - Ivo Timoteo
- Friday 16 May 2014, 14:00-15:00
- LT2, Computer Laboratory, William Gates Building.
If you have a question about this talk, please contact Tadas Baltrusaitis. Probabilistic graphical models are commonly used in the machine learning community. They provide a simple way to design and visualize the structure of the probability model and allow some complex computations to be expressed in terms of graphical manipulations. Inference in those models has been widely studied and is included to some extent in the undergraduate courses (AI2). However, these assume that the structure of the graph is known a priori and remains unaltered. In this lecture I will focus on the most common methods used to infer the structure of Bayesian networks, that is, it underlying graph, directly from the data. This talk is part of the Computer Laboratory Research Students' Lectures 2014 series. ## This talk is included in these lists:- Computer Laboratory Research Students' Lectures 2014
- LT2, Computer Laboratory, William Gates Building
Note that ex-directory lists are not shown. |
## Other listsBiodiversity and genomics Birational geometry seminar Wolfson Informal Lunchtime Seminar Series## Other talksMy Life in Science Seminar â€œPublishing in Science: an Inside Look" Statistical Methods in Pre- and Clinical Drug Development: Tumour Growth-Inhibition Model Example Measuring Designing: Design Cognitiometrics, Physiometrics & Neurometrics Elizabeth Bowen's Writings of the Second World War Small Opuntioideae Cosmology from the Kilo-Degree Survey |