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Algorithms & Computationally Intensive Inference seminars

2020/2021(Term 1): The seminar will happen on Microsoft Teams on Fridays 13:00 (or occasionally at different times).

If you are not affiliated with Warwick and wish to attend our seminars please register here.

If you would like to speak, or you want to be included in any emails, please contact one of the organisers.

Current Organisers: Massimiliano Tamborrino, Jure Vogrinc

Website URL:

Mailing List Sign-Up:

Mailing List: (NB - only approved members can post)

Microsoft Teams Link (username/password same as for eduroam) available here

Next talk: Friday 30th October, 4pm UK time

Philippe Gagnon: Lifted samplers for partially ordered discrete state-spaces

Lifting is a well known technique employed to avoid that the Markov chains used as samples backtrack too often. It consists in lifting the state-space to include direction variables for guiding the Markov chains. Its implementation is direct when the probability mass function targeted is defined on a totally ordered set, such as that of a integer-valued random variables In this paper, we adapt this technique to the situation where only a partial order can be established on the sampling space and explore its benefits. Important applications include the simulation of systems formed from binary variables, such as those arising in the Ising model, and variable selection when the posterior model probabilities can be evaluated, up to a normalising constant. To accommodate for the situation where one does not have access to these marginal model probabilities, a lifted trans-dimensional sampler for partially ordered model spaces is introduced. We show through theoretical analyses and empirical results that the lifted samplers outperform their non-lifted counterparts in some situations, but not always, achieving this at no extra computational cost. The code to reproduce all experiments is available online.

2020/21 Term 1 (and September)

This is the list of confirmed speakers, which will be continuously updated.

Date Speaker Title Abstract Slides Video
Week -2: 18/09 (12 UK time) Clara Grazian Approximate Bayesian analysis of (un)conditional copulas Abstract Slides Video
Week -1: 25/09 Joe Meagher Bayesian Ancestral Reconstruction for Bat Echolocation Abstract Slides Video
Week0: 02/10 Michael Choi On the convergence of an improved and adaptive kinetic simulated annealing Abstract Slides  
Week 1: 09/10   cancelled (OxWaSP workshop)      
Week 2: 16/10 (2pm UK time) Liangliang Wang Sequential Monte Carlo for estimating parameters of differential equations Abstract Slides  
Week 3: 23/10 Sebastian Vollmer

Part 1: Risk prediction and Risk prediction

Part 2: Machine Learning in Julia and benchmarking results on predictive fairness.

Week 4: 30/10 (4pm UK time) Philippe Gagnon Lifted samplers for partially ordered discrete state-spaces      
Week 5: 6/11 Richard Everitt        
Week 6: 13/11 Letizia Angeli        
Week 7: 20/11 Yan Qu        
Week 8: 27/11
(4pm UK time)
Caroline Colijn
COVID-19 data sources and their challenges for modelling and estimation
Week 9: 4/12 Suzie Brown        
Week 10: 11/12 Daniel Jerison        

2020/21 Term 2:

Date Speaker Title Abstract Slides Video
Week 1: 15/1 Jeremias Knoblauch        
Week 2: 22/1 TBA        
Week 3: 29/1 TBA        
Week 4: 5/2 TBA        
Week 5: 12/2 TBA        
Week 6: 19/2 TBA        
Week 7: 26/2 TBA        
Week 8: 5/3 TBA        
Week 9: 12/3 TBA        
Week 10: 19/3 TBA        

2020/21 Term 3:

Date Speaker Title Abstract Slides Video
Week 1: 30/4 TBA        
Week 2: 7/5 TBA        
Week 3: 14/5 TBA        
Week 4: 21/5 TBA        
Week 5: 28/5 TBA        
Week 6: 4/6 TBA        
Week 7: 11/6 TBA        
Week 8: 18/6 TBA        
Week 9: 25/6 TBA        
Week 10: 2/7 TBA        

Previous Years:











Some key phrases:

- Sampling and inference for diffusions
- Exact algorithms
- Intractable likelihood
- Pseudo-marginal algorithms
- Particle filters
- Importance sampling
- Adaptive MCMC
- Perfect simulation
- Markov chains
- Random structures
- Randomised algorithms