7 episodes

The Department of Statistics at Oxford is a world leader in research including computational statistics and statistical methodology, applied probability, bioinformatics and mathematical genetics. In the 2014 Research Excellence Framework (REF), Oxford's Mathematical Sciences submission was ranked overall best in the UK.
This is an exciting time for the Department. We have now moved into our new home on St Giles and we are currently settling in.

The new building provides improved lecture and teaching space, a variety of interaction areas, and brings together researchers in Probability and Statistics. It has created a highly visible centre for the Department in Oxford.

Since 2010, the Department has been awarded over forty research grants with a total value of £9M, not counting several very large EPSRC and MRC funded awards for Centres for doctoral training.The main sponsors are the European Commission, EPSRC, the Medical Research Council and the Wellcome Trust.

We offer an undergraduate degree (BA or MMath) in Mathematics and Statistics, jointly with the Mathematical Institute.

At postgraduate level there is an MSc course in Applied Statistics, as well as a lively and stimulating environment for postgraduate research (DPhil or MSc by Research). Our graduates are employed in a wide range of occupational sectors throughout the world, including the university sector.

The Department co-hosts the EPSRC and MRC Centre for Doctoral Training (CDT) in Next-Generational Statistical Science- the Oxford-Warwick Statistics Programme OxWaSP.

Department of Statistics Oxford University

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    • 4.0, 1 Rating

The Department of Statistics at Oxford is a world leader in research including computational statistics and statistical methodology, applied probability, bioinformatics and mathematical genetics. In the 2014 Research Excellence Framework (REF), Oxford's Mathematical Sciences submission was ranked overall best in the UK.
This is an exciting time for the Department. We have now moved into our new home on St Giles and we are currently settling in.

The new building provides improved lecture and teaching space, a variety of interaction areas, and brings together researchers in Probability and Statistics. It has created a highly visible centre for the Department in Oxford.

Since 2010, the Department has been awarded over forty research grants with a total value of £9M, not counting several very large EPSRC and MRC funded awards for Centres for doctoral training.The main sponsors are the European Commission, EPSRC, the Medical Research Council and the Wellcome Trust.

We offer an undergraduate degree (BA or MMath) in Mathematics and Statistics, jointly with the Mathematical Institute.

At postgraduate level there is an MSc course in Applied Statistics, as well as a lively and stimulating environment for postgraduate research (DPhil or MSc by Research). Our graduates are employed in a wide range of occupational sectors throughout the world, including the university sector.

The Department co-hosts the EPSRC and MRC Centre for Doctoral Training (CDT) in Next-Generational Statistical Science- the Oxford-Warwick Statistics Programme OxWaSP.

    • video
    The Science Media Centre and its work

    The Science Media Centre and its work

    Fiona Lethbridge, Science Media Centre, gives a talk on the Science Media Centre and it's work. Fiona is a senior press officer at the Science Media Centre and has worked there since July 2012. She has a PhD in evolutionary biology from the University of Edinburgh. The Science Media Centre is an independent press office which opened in 2002 and believes that scientists can have a huge impact on the way the media cover scientific issues, by engaging quickly and effectively with the stories that are influencing public debate and attitudes to science, by speaking to journalists when they need their help. The SMC’s philosophy is that ‘The media will DO science better when scientists DO the media better.’ The SMC aims to help improve the accuracy and evidence-base of media reporting on the big and controversial science, health and environment news of the day, working on stories from GM, fracking and Fukushima to statins, e-cigarettes, antidepressants and the coronavirus.
    Please could you name the talk by its title above rather than 'Careers Event talk'

    • 28 min
    • video
    How To Set Up Continuous Integration to Make Your Code More Robust, More Maintainable, and Easier to Publish

    How To Set Up Continuous Integration to Make Your Code More Robust, More Maintainable, and Easier to Publish

    Dr Fergus Cooper, Research Software Engineer, Oxford RSE Group, gives a talk for the department of Statistics on 5th June 2020. Following on from Graham Lee's talk on automated testing, we will use GitHub actions to automate the testing of a small Python project. We will: recap why this might be a good idea; walk through setting up a workflow on GitHub; test our code against multiple Python versions on multiple operating systems; and integrate other services such as code coverage and automated documentation generation.

    Dr Fergus Cooper is a member of the Oxford Research Software Engineering group, which he co-founded in 2018 after finishing a DPhil in the Mathematical Institute. His research background is computational biology where he developed agent-based models of the developing tooth placode. He is now a passionate advocate for software best practices in academia, and will talk to anyone about modern C++."

    • 44 min
    • video
    Developing better code with automated testing

    Developing better code with automated testing

    Graham Lee, Research Software Engineer, Oxford RSE Group, gives talk for the department of Statistics on 22nd May 2020. Abstract: If we want reliable, reproducible simulations and data analysis software, we need to know that we have implemented our code correctly. Further, we need to be confident that changes we make to the code do not introduce unintended flaws. Automated testing is a technique widely used in industry to capture information about the expected behaviour of software and ensure that the system retains that behaviour through its evolution. In this talk, Graham explores the application of the technique to scientific software.

    • 45 min
    • video
    Cluster-Randomised Test Negative Designs: Inference and Application to Vector Trials to Eliminate Dengue

    Cluster-Randomised Test Negative Designs: Inference and Application to Vector Trials to Eliminate Dengue

    Nick Jewell, University of California, Berkeley School of Public Health, gives a talk for the departmental of Statistics on 28th May 2020. Abstract: The successful introduction of the intracellular bacterium Wolbachia into Aedes aegypti mosquitoes enables a practical approach for dengue prevention through release of Wolbachia-infected mosquitoes. Wolbachia reduces dengue virus replication in the mosquito and, once established in the mosquito population, it is possible that this will provide a long-term and sustainable approach to reducing or eliminating dengue transmission. A critical next step is to assess the efficacy of Wolbachia deployments in reducing dengue virus transmission in the field. I will describe and discuss the statistical design of a large-scale cluster randomised test-negative parallel arm study to measure the efficacy of such interventions. Comparison of permutation inferential approaches to model based methods will be described. Extensions to allow for individual covariates, and alternate designs such as the stepped wedge approach, will also be briefly introduced. There are also interesting questions regarding interrupted time-series methods associated with analysing pilot site data.

    • 1 hr 2 min
    • video
    MCMC for Hierachical Bayesian Models Using Non-reversible Langevin Methods

    MCMC for Hierachical Bayesian Models Using Non-reversible Langevin Methods

    Radford M. Neal (University of Toronto), gives a talk for the department of Statistics. Hamiltonian Monte Carlo (HMC) is an attractive MCMC method for continuous distributions because it makes use of the gradient of the log probability density to propose points far from the current point, avoiding slow exploration by a random walk (RW). The Langevin method - equivalent to HMC with one leapfrog step - also uses gradient information, but is slow due to RW behaviour. In this talk, I discuss how the Langevin method can be made competitive with HMC using two modifications that suppress RW behaviour by making the Markov chain no longer be time-reversible. This modified Langevin method can be better than HMC when it is necessary to combine gradient-based updates with other updates. One use is when other updates are required for discrete variables. Another application is learning of hierarchical Bayesian neural networks models, for which hyperparameters controlling the properties of the function learned are difficult to update using HMC. If they are instead updated by, for example, Gibbs sampling, these updates can be done more often, improving sampling efficiency, when a non-reversible Langevin method is used rather than HMC with long trajectories.

    • 1 hr 4 min
    • video
    Maths and Stats in Action – Real-time Analysis to Understand the Novel Coronavirus

    Maths and Stats in Action – Real-time Analysis to Understand the Novel Coronavirus

    Providing a whirlwind tour of the quantitative analyses currently underway to understand the transmission and control of the novel coronavirus (2019-nCOV). Recorded on 31st January 2020.
    www.imperial.ac.uk -> mrc-global-infectious-disease-analysis -> News--wuhan-coronavirus LINK - https://tinyurl.com/mrc-global-infectious-disease

    Biology Preprint paper:
    2019-20 Wuhan coronavirus outbreak: Intense surveillance is vital for preventing sustained transmission in new locations
    LINK - https://www.biorxiv.org/content/10.1101/2020.01.24.919159v1

    • 39 min

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