Algorithms & Computationally Intensive Inference seminars
2020/2021: The seminars will happen on Microsoft Teams on Fridays 1pm UK time (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: www.warwick.ac.uk/compstat
Mailing List Sign-Up: http://mailman1.csv.warwick.ac.uk/mailman/listinfo/algorithmseminar
Mailing List: algorithmseminar@listserv.csv.warwick.ac.uk (NB - only approved members can post)
Microsoft Teams Link (username/password same as for eduroam) available here
Next talk, Friday 22/1 at 4pm UK time:
Jeffrey Rosenthal: MCMC Confidence Intervals and Biases Without CLTs
This week will exceptionally happen on zoom. Pass: 450131
Abstract: Most estimates of MCMC errors rely on versions of the Markov chain Central Limit Theorem (CLT), which might not always hold and can be difficult to verify. I will discuss an alternative approach using just Chebychev’s inequality together with basic variance estimates, which provides slightly larger confidence intervals (2.3 times as wide) without requiring any CLT. This approach in turn raises questions about bounds on MCMC bias and its relation to Markov chain convergence rates.
2020/21 Term 2:
Date | Speaker | Title | Abstract | Slides | Video |
Week 1: 15/1 (4pm UK time) | Jeremias Knoblauch | Postponed to 12/2 at 4pm | Abstract | ||
Week 2: 22/1 (4pm UK time) | Jeffrey Rosenthal | MCMC Confidence Intervals and Biases Without CLTs | Abstract | ||
Week 3: 29/1 | Paul Dobson | ||||
Week 4: 5/2 | Daniel Jerison | ||||
Week 5: 12/2 | Jaromir Sant | ||||
Week 5: 12/2 (4pm UK time) |
Jeremias Knoblauch | Optimization-centric Generalizations of Bayesian Inference | Abstract | ||
Week 6: 19/2 | Xenia Miscouridou | ||||
Week 7: 26/2 | Nianqiao (Phyllis) Ju | ||||
Week 8: 5/3 | Adeline Samson | ||||
Week 9: 12/3 (4 pm UK time) | Ari Stern | ||||
Week 10: 19/3 | TBA |
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 |
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 | Video |
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 | Video |
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. |
Abstract | Video | |
Week 4: 30/10 (4pm UK time) | Philippe Gagnon | Lifted samplers for partially ordered discrete state-spaces | Abstract | Video | |
Week 5: 6/11 | Richard Everitt |
Rare event ABC-SMC^2 |
Abstract | Slides | Video |
Week 6: 13/11 | Letizia Angeli | Interacting Particle Systems Approximations of Feynman-Kac Formulae in continuous time | Abstract | Slides | Video |
Week 7: 20/11 | Yan Qu | Exact Simulation of Self-Excited Point Process with Levy Driven OU | Abstract | ||
Week 8: 27/11 (4pm UK time) |
Caroline Colijn |
COVID-19 data sources and their challenges for modelling and estimation
|
Abstract | ||
Week 9: 4/12 | Suzie Brown | Asymptotic genealogies of sequential Monte Carlo algorithms | Abstract | ||
Week 10: 11/12 | cancelled |
Previous Years:
Some key phrases:
- Sampling and inference for diffusions
- Exact algorithms
- Intractable likelihood
- Pseudo-marginal algorithms
- Particle filters
- Importance sampling
- MCMC
- Adaptive MCMC
- Perfect simulation
- Markov chains
- Random structures
- Randomised algorithms