The complexity and sheer size of modern data sets, of which ever increasingly demanding questions are posed, give rise to major challenges and opportunities for modern statistics. While likelihood-based statistical methods still provide the gold standard for statistical methodology, the applicability of existing likelihood methods to the most demanding of modern problems is currently limited. Thus traditional methodologies for numerical optimisation of likelihoods, and for simulating from complicated posterior distributions, such as Markov chain Monte Carlo and Sequential Monte Carlo algorithms often scale poorly with data size and model complexity, and thus fail for the most complex of modern problems.
The area of computational statistics is currently developing extremely rapidly, motivated by the challenges of the recent big data revolution, and enriched by new ideas from machine learning, multi-processor computing, probability and applied mathematical analysis. Motivation for this development comes from across the physical biological and social sciences, including physics, chemistry, astronomy, epidemiology, medicine, genetics, sociology, economics - in fact it is hard to find problems not enriched by big data and the resultant associated statistical challenges.
This programme will focus on methods associated with likelihood, its variants and approximations, taking advantage of, and creating new advances in statistical methodology. These advances have the potential to impact on all aspects of science and industry that rely on probabilistic models for learning from observational or experimental data.
Intractable likelihood problems are defined loosely as ones where the repeated evaluation of likelihood function (as required in standard algorithms for likelihood-based inference) is impossible or too computationally expensive to carry out. Scalable methods for carrying out statistical inference are loosely defined to be methods whose computational cost and statistical validity scale well with both model complexity and data size.
Understanding and developing scalable methods for intractable likelihood problems requires expertise across statistics, computer science, probability and numerical analysis. Thus it is imperative that the programme be broad, covering statistical, algorithmic and computational aspects of inference. The programme will cut across the traditional boundary between frequentist and Bayesian inference, and will incorporate both statistics and machine learning approaches to inference. Central to the focus will be the close integration of algorithm optimisation with the opportunities offered, and constraints imposed by modern multi-core technologies such as GPUs.
The first week of the programme will feature a broad-focused workshop, and more application specific activities will take place later.
Please note that the programme will also feature a one day workshop entitled "Sampling methods in statistical physics and Bayesian inference". This will take place on 18 July 2017 and attendance is free. To register your place please email samuel.livingstone[at]bristol.ac[dot]uk (statistics) or michael.faulkner[at]bristol.ac[dot]uk (physics). Further details below.
The Institute kindly requests that any papers published as a result of this programme’s activities are credited as such. Please acknowledge the support of the Institute in your paper using the following text:
The author(s) would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support and hospitality during the programme Scalable inference; statistical, algorithmic, computational aspects, where work on this paper was undertaken. This work was supported by EPSRC grant EP/K032208/1.
3 July 2017 to 7 July 2017
|Tuesday 18th July 2017
|10:20 to 11:00
Joris Bierkens Delft University of Technology
|11:00 to 11:40
Robert Jack University of Bath
|12:10 to 12:50
|12:50 to 13:30
Michela Ottobre Heriot-Watt University
|15:00 to 15:40
Anthony Maggs City of Paris Industrial Physics and Chemistry Higher Educational Institution, (ESPCI)
|15:40 to 16:20
Andrew Duncan Imperial College London
Subscribe for the latest updates on events and news
INI is a creative collaborative space which is occupied by up to fifty-five mathematical scientists at any one time (and many more when there is a workshop). Some of them may not have met before and others may not realise the relevance of other research to their own work.
INI is especially important as a forum where early-career researchers meet senior colleagues and form networks that last a lifetime.
Here you can learn about all activities past, present and future, watch live seminars and submit your own proposals for research programmes.
Within this section of the website you should find all the information required to arrange and plan your visit to the Institute. If you have any further questions, or are unable to find the information you require, please get in touch with the relevant staff member or our Reception team via our contact pages.
INI and its programme participants produce a range of publications to communicate information about activities and events, publish research outcomes, and document case studies which are written for a non-technical audience. You will find access to them all in this section.
The Isaac Newton Institute aims to maximise the benefit of its scientific programmes to the UK mathematical science community in a variety of ways.
Whether spreading research opportunities through its network of correspondents, offering summer schools to early career researchers, or hosting public-facing lectures through events such as the Cambridge Festival, there is always a great deal of activity to catch up on.
Find out about all of these endeavours in this section of the site.
There are various ways to keep up-to-date with current events and happenings at the Isaac Newton Institute. As detailed via the menu links within this section, our output covers social media streams, news articles, a regular podcast series, an online newsletter, and more detailed documents produced throughout the year.
“A world famous place for research in the mathematical sciences with a reputation for efficient management and a warm welcome for visitors”
The Isaac Newton Institute is a national and international visitor research institute. It runs research programmes on selected themes in mathematics and the mathematical sciences with applications over a wide range of science and technology. It attracts leading mathematical scientists from the UK and overseas to interact in research over an extended period.
INI has a vital national role, building on many strengths that already exist in UK universities, aiming to generate a new vitality through stimulating and nurturing research throughout the country.During each scientific programme new collaborations are made and ideas and expertise are exchanged and catalysed through lectures, seminars and informal interaction, which the INI building has been designed specifically to encourage.
For INI’s knowledge exchange arm, please see the Newton Gateway to Mathematics.
The Institute depends upon donations, as well as research grants, to support the world class research undertaken by participants in its programmes.
Fundraising activities are supported by a Development Board comprising leading figures in academia, industry and commerce.
Visit this section to learn more about how you could play a part in supporting INI’s groundbreaking research.
In this section you can find contact information, staff lists, maps and details of how to find INI’s main building in Cambridge.
Our administrative staff can help you with any queries regarding a prospective or planned visit. If you would like to discuss a proposed a research programme or other event, our senior management team will be happy to help.