Archive for University of Warwick

ABC with path signatures [One World ABC seminar, 2/2/23]

Posted in Books, pictures, Running, Statistics, Travel, University life with tags , , , , , , , on January 29, 2023 by xi'an

The next One World ABC seminar is by Joel Dyer (Oxford) at 1:30pm (UK time) on 02 February.

Title: Approximate Bayesian Computation with Path Signatures

Abstract: Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable. Approximate Bayesian computation (ABC) generates likelihood-free posterior samples by comparing simulated and observed data through some distance measure, but existing approaches are often poorly suited to time series simulators, for example due to an independent and identically distributed data assumption. In this talk, we will discuss our work on the use of path signatures in ABC as a means to handling the sequential nature of time series data of different kinds. We will begin by discussing popular approaches to ABC and how they may be extended to time series simulators. We will then introduce path signatures, and discuss how signatures naturally lead to two instances of ABC for time series simulators. Finally, we will demonstrate that the resulting signature-based ABC procedures can produce competitive Bayesian parameter inference for simulators generating univariate, multivariate, irregularly spaced, and even non-Euclidean sequences.

Reference: J. Dyer, P. Cannon, S. M Schmon (2022). Approximate Bayesian Computation with Path Signatures. arXiv preprint 2106.12555

diffusion means in geometric statistics [CRiSM Seminar]

Posted in Statistics with tags , , , , , on November 16, 2022 by xi'an

Adversarial Bayesian Simulation [One World ABC’minar]

Posted in Statistics with tags , , , , , , , , , on November 15, 2022 by xi'an

The next One World ABC webinar will take place on 24 November, at 1:30 UK Time (GMT) and will be presented by Yi Yuexi Wang (University of Chicago) on “Adversarial Bayesian Simulation”, available on arXiv. [The link to the webinar is available to those who have registered.]

In the absence of explicit or tractable likelihoods, Bayesians often resort to approximate Bayesian computation (ABC) for inference. In this talk, we will cover two summary-free ABC approaches, both inspired by adversarial learning. The first one adopts a classification-based KL estimator to quantify the discrepancy between real and simulated datasets. We consider the traditional accept/reject kernel as well as an exponential weighting scheme which does not require the ABC acceptance threshold. In the second paper, we develop a Bayesian GAN (B-GAN) sampler that directly targets the posterior by solving an adversarial optimization problem. B-GAN is driven by a deterministic mapping learned on the ABC reference by conditional GANs. Once the mapping has been trained, iid posterior samples are obtained by filtering noise at a negligible additional cost. We propose two post-processing local refinements using (1) data-driven proposals with importance reweighting, and (2) variational Bayes. For both methods, we support our findings with frequentist-Bayesian theoretical results and highly competitive performance in empirical analysis. (Joint work with Veronika Rockova)

four positions in statistics at Warwick [reposted]

Posted in University life with tags , , , , , , , , , , on November 12, 2022 by xi'an

Assistant and Associate Professor positions in Statistics and Machine Learning at Warwick

Outstanding and enthusiastic academics are sought by the Department of Statistics at Warwick, one of the world’s most prominent and most research active departments of Statistics. The Department has close relations with the co-located Mathematics Institute and Department of Computer Science and with other departments such as Economics and the Warwick Business School. Four permanent posts are available, which reflects the strong commitment of the University of Warwick to invest in Statistics and Machine Learning:

Assistant Professor, Applied Statistics

Associate Professor, Machine Learning

Assistant Professor, Machine Learning

Associate Professor, Statistics

Applicants should have evidence or promise of world-class research excellence and ability to deliver high quality teaching across our broad range of degree programmes. At Associate Professor level, applicants should have an outstanding publication record. Other positive indicators include enthusiasm for engagement with other disciplines, within and outside the Department and, at Associate Professor level, a proven ability to secure research funding. Further details of the requirements for each of the four positions can be found at https://warwick.ac.uk/statjobs.

The Department of Statistics is committed to promoting equality and diversity, holding an Athena SWAN Silver award which demonstrates this commitment. We welcome applicants from all sections of the community and will give due consideration to applicants seeking flexible working patterns, and to those who have taken a career break. Further information about working at the University of Warwick, including information about childcare provision, career development and relocation is at https://warwick.ac.uk/services/humanresources/workinghere/.

Informal inquiries can be addressed to Professors Jon Forster (J.J.Forster@warwick.ac.uk) or Adam Johansen (A.M.Johansen@warwick.ac.uk) or to any other senior member of the Warwick Statistics Department.

Closing date: December 12, 2022.

More details and a link to the application forms: https://warwick.ac.uk/statjobs

Further information about the Department of Statistics: https://warwick.ac.uk/stats

Further information about the University of Warwick: https://www2.warwick.ac.uk/services/humanresources/jobsintro/furtherparticulars

 

Data science for social good fellowships [DSSGx UK 2023]

Posted in Kids, Statistics, Travel, University life with tags , , , , , , , , , , , , , on November 9, 2022 by xi'an

Warwick is (again) running a 12-week summer programme bringing together some of the top student talents from data science, machine learning and artificial intelligence, all over the World, to work on real-world data science challenges and deliver positive social impact. Applications for DSSG 2023 are now OPEN! Click here for the application form (please read the information carefully) and click here for the FAQs for 2023. (The application also works for a similar programme in Kaiserslauten, Germany.

DSSG helps not-for-profit organisations and government bodies to achieve more with their data by enhancing their services, interventions and outreach, helping fulfil their mission of improving the world and people’s lives.

The programme gives not-for-profit organisations and government bodies unprecedented access to inspiring, top-tier data science talent. This helps build their capacity to use cutting-edge quantitative methods to address societal challenges in areas such as education, health, energy, public safety, transportation and economic development.

At the same time, it provides intensive case-based and supported training to students to create industry-standard data science products in collaboration with government agencies and NGOs, to deliver positive social impact. And it builds a world-wide community of data scientists who care about the social good.

In 2019, the University of Warwick together with the Alan Turing Institute brought DSSG to the UK. The University of Warwick has run it each year since and now preparation is well underway for DSSGx UK 2023, which will be held at the University of Warwick, UK, from 5 June to 25 August.

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