Algorithms & Computationally Intensive Inference seminars

Terms 1-3, Location MB2.23, Fridays 12:15-14:00 (12:15-12:45 is an informal sandwich lunch).

Reminder emails are not sent to participants unless there is a change to the scheduled programme at short notice. If you would like to speak, or you want to be included in any emails, please contact one of the organisers.

Current Organisers: Dootika Vats,

If you would like to talk, or have ideas for possible speakers, then please email one of the organisers above.

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)

2018/19 Term 1:

Week 1 - 5th October - Cancelled
Week 2 - 12th October - Summary of research short talks:
Week 3 - 19th October - Summary of research short talks:
Week 4 - 26th October - Giulio Morina (Warwick) - "From the Bernoulli Factory to a Dice Enterprise via Perfect Sampling of Markov Chains"
- Abstract: Given a p-coin that lands heads with unknown probability p, we wish to construct an algorithm that produces an f(p)-coin for a given function f:(0, 1)→(0, 1). This problem is commonly known as the Bernoulli
  Factory and generic ways to design a practical algorithm for a given function f exist only in a few special cases. We present a constructive way to build an efficient Bernoulli Factory when f(p) is a rational function. Moreover, we extend the original problem to a more general setting where we have access to an m-sided
  die and we wish to roll a v-sided one, where the probability of rolling each face is a fixed function of the original probabilties. We achieve this by perfectly simulating from the stationary distribution of a
  certain class of Markov chains.
Week 5 - 2nd November - Cancelled. CoSInES Launch Day.
Week 6 - 9th November - Iker Perez (Nottingham) - "Novel approaches to efficiently augment a Markov Jump process for exact Bayesian Inference"
- Abstract: In this talk we will discuss foundational statistical challenges associated with families of Markovian jump models, which often find applications in domains such as genetics, epidemiology, mathematical biology or queueing theory. We will first review Markov jump processes, and by means of common accessible examples, illustrate the computational impediments posed by real-world application scenarios to inverse uncertainty quantification tasks. Then, we will give an overview of the recent advances linked to structured jump systems. Our work is concerned with building on uniformization procedures and we propose a novel efficient auxiliary-variable algorithm for data augmentation, which yields computationally tractable distributions suited for exact (in Monte Carlo sense) Bayesian inference in often large, infinite or multivariate population systems. We demonstrate the capabilities of the presented methods by drawing Bayesian inference for partially observed stochastic epidemics and show that it overcomes the limitations of existing vanilla approaches. This is joint work (in progress) with Theo Kypraios.
Week 7 - 16th November - Ritabrata Dutta (Warwick) - "Well-Tempered Hamiltonian Monte Carlo on Active-Space"
- Abstract: When the gradient of the log-target distribution is available, Hamiltonian Monte
  Carlo (HMC) has been proved to be an efficient simulation algorithm. However,
  HMC performs poorly when the target is high-dimensional and has multiple isolated
  modes. To alleviate these problems we propose to perform HMC on a locally and
  continuously tempered target distribution. This tempering is based on an efficient
  approach to simulate molecular dynamics in high-dimensional space, known as well-
  tempered meta-dynamics. The tempering we suggest is performed locally and only
  along the directions of the maximum changes in the target which we identify as
  the active space of the target. The active space is the span of the eigenfunctions
  corresponding to the dominant eigenvalues of the expected Hessian matrix of the
  log-target. To capture the state dependent non-linearity of the target, we iteratively
  estimate the active space from the most recent batch of samples obtained from the
  simulation. Finally, we suggest a re-weighting scheme based on path-sampling to
  provide importance weights for the samples drawn from the continuously-tempered
  distribution. We illustrate the performance of this scheme for target distributions
  with complex geometry and multiple modes on high-dimensional spaces in comparison
  with traditional HMC with No-U-Turn-Sampler.
Week 8 - 23rd November - Stephen Connor (York) - Title / Abstract TBA
Week 9 - 30th November - Emilia Pompe (Oxford) - Title / Abstract TBA
Week 10 - 7th December - Flávio Gonçalves (UFMG) - Title / Abstract TBA

2018/19 Term 2:

Week 1 - 11th January- Neil Chada (NUS) - Title / Abstract TBA
Week 2 - 18th January - James Flegal (UC Riverside) - Title / Abstract TBA (in room MB2.22)
Week 3 - 25th January - Joint Session (Short Talks)
- Jure Vogrinc (Warwick) - Title / Abstract TBA
- Georgios Vasdekis (Warwick) - Title / Abstract TBA
Week 4 - 1st February - Mateusz Majka (Warwick) - Title / Abstract TBA
Week 5 - 8th February - Ioannis Kosmidis (Warwick) - Title / Abstract TBA
Week 6 - 15th February - Available
Week 7 - 22nd February - Patrick Rebeschini (Oxford) - Title / Abstract TBA
Week 8 - 1st March - Susana Gomes (Warwick) - Title / Abstract TBA
Week 9 - 8th March - Available
Week 10 - 15th March - Available

2018/19 Term 3:

Week 1 - 26th April - Available
Week 2 - 3rd May - Available
Week 3 - 10th May - Available
Week 4 - 17th May - Available
Week 5 - 24th May - Available
Week 6 - 31st May - Available
Week 7 - 7th June - Available
Week 8 - 14th June - Available
Week 9 - 21st June - Available
Week 10 - 28th June - Available

Previous Years:

2010/2011

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...

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