Details of Grant 

EPSRC Reference:	EP/K014463/1
Title:	Intractable Likelihood: New Challenges from Modern Applications (ILike)
Principal Investigator:	Roberts, Professor G O
Other Investigators:	Firth, Professor D Holmes, Professor C Andrieu, Professor C Fearnhead, Professor P
Researcher Co-Investigators:
Project Partners:
Department:	Statistics
Organisation:	University of Warwick
Scheme:	Programme Grants
Starts:	01 January 2013	Ends:	30 June 2018	Value (£):	2,369,503
EPSRC Research Topic Classifications:	Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:
Panel History:	Panel Date Panel Name Outcome 18 Sep 2012 Mathematics Prioritisation Panel Meeting September 2012 Announced
Summary on Grant Application Form
In most statistical contexts, it is recognised that inference methodology based on the likelihood function are usually methods of choice. However such methods are not always easy to implement. For instance, in complex problems often with massive data sets, it can sometimes be completely impossible to even evaluate the likelihood function. The computational statistics revolution of the 1990s provided powerful methodology for carrying out likelihood-based inference, including Markov chain Monte Carlo methods, the EM algorithm, many associated optimisation techniques for likelihoods, and Sequential Monte Carlo methods. Although these methods have been and are highly successful in making likelihood-based inference accessible to a wide range of problems from virtually every area of science and technology, we now have a far better understanding of their limitations, for example in high-dimensional problems and for massive data sets. Thus many challenging statistical inference problems of the 21st century cannot be addressed using existing likelihood-based methods. Examples which motivate the current project come from genetics, genomics, infectious disease epidemiology, ecology, commerce, and bibliometrics. However there have been various recent breakthroughs in computational and statistical approaches to intractable likelihood problems, including pseudo-marginal and particle MCMC, likelihood-free methods such as Approximate Bayesian Computation, composite and pseudo-likelihoods, new simulation methods for hitherto intractable stochastic models, and adaptive Monte Carlo methods. These advances coupled with developments in multi-core computational technologies such as GPUs, have enormous potential for extending likelihood methods to meet the most difficult challenges of modern scientific questions. All these new areas have demonstrated considerable promise. However, a unified approach is required to deliver a step change in our capability to implement likelihood. The Ilike workplan will initially involve the investigation of 10 projects all combining two or more of the about breakthrough areas. The second half of the project will involve a similar workplan informed by the outcomes of the initial projects.
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.warwick.ac.uk