Research lectures, seminars and events
The events listed in this area are research seminars, workshops and lectures hosted by Durham University departments and research institutes. If you are not a member of the University, but wish to enquire about attending one of the events please contact the organiser or host department.
|October 2020||December 2020|
Events for 16 November 2020
Statistical models which impose restrictions on marginal distributions of categorical data have received considerable attention especially in social and economic sciences. A particular appealing class is that of log-linear marginal models introduced by Bergsma and Rudas (2002) that have been used to provide parameterisations for discrete graphical models of marginal independence. Bayesian analysis of Graphical Log-Linear Marginal Models has not been developed as much as traditional methods. No conjugate analysis is available and MCMC methods must be employed. The likelihood of the model cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of the probability parameters. Hence, at each step of the MCMC an iterative procedure needs to be applied in order to calculate the cell probabilities and consequently the model likelihood. Finally, in order to have a well-defined model of marginal independence, the considered MCMC algorithm should generate parameter values leading to a joint probability distribution with compatible marginals. Possible solutions to the previously discussed problems will be presented.
This talk is based on recent papers with Ioannis Ntzoufras (AUEB) and Monia Lupparelli (UNIFI)
Dr Jeremiah Coogan: Repetition and Difference: Locating Eusebiusâ€™s Fourfold Gospel in the history of Gospel Writing
Contact firstname.lastname@example.org for more information about this event.
Machine learning is soooooo 2019, Quantum Machine learning is what all the hip cool kids do now.
In this talk we will briefly introduce "classical" neural networks and a quantum extension known as a Variational Quantum Classifier. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems.
The talk will mostly be pedagogical, acting as an introduction to the subject, and hopefully will be approachable to those who haven't used "regular" ML before. I will also discuss the results of some of my recent research. We have applied our QML model to a resonance search in di-top final states. We find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method.
Quantum machine learning may sound like a meme but I promise its mostly not.