Scalable inference; statistical, algorithmic, computational aspects
Created: | 2017-07-19 10:48 |
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Institution: | Isaac Newton Institute for Mathematical Sciences |
Description: | 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. |
Media items
This collection contains 36 media items.
Media items
Asymptotics of Approximate Bayesian Computation
Fearnhead, P
Wednesday 5th July 2017 - 09:00 to 09:45
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jul 2017
Category theory and functional programming for scalable statistical modelling and computational inference
Wilkinson, D
Tuesday 4th July 2017 - 16:15 to 17:00
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jul 2017
Community recovery in weighted stochastic block models
Loh, P
Thursday 6th July 2017 - 11:45 to 12:30
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 24 Jul 2017
Comparing Consensus Monte Carlo Strategies for Distributed Bayesian Computation
Scott, S
Tuesday 4th July 2017 - 11:00 to 11:45
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jul 2017
Coresets for scalable Bayesian logistic regression
Broderick, T
Tuesday 4th July 2017 - 13:30 to 14:15
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jul 2017
Data compression with statistical guarantees
Richardson, S
Monday 3rd July 2017 - 13:30 to 14:15
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jul 2017
Detecting radiological anomalies
Scott, J
Thursday 6th July 2017 - 11:00 to 11:45
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 24 Jul 2017
Event-chain algorithms: taming randomness in Monte Carlo methods through irreversibility, factorization and lifting
Manon, M
Tuesday 18th July 2017 - 12:10 to 12:50
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 19 Jul 2017
Exact Bayesian Inference for Big Data: Single- and Multi-Core Approaches
Pollock, M
Wednesday 5th July 2017 - 13:30 to 14:15
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jul 2017
Exact Sampling for Multivariate Diffusions
Blanchet, J
Friday 7th July 2017 - 11:00 to 11:45
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 24 Jul 2017
Fast Bayesian Boolean Matrix Factorisation
Holmes, C
Wednesday 5th July 2017 - 15:30 to 16:15
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jul 2017
Hierarchy-preserving regularization solution paths for identifying interactions in high dimensional data
Zhang, H
Thursday 6th July 2017 - 09:00 to 09:45
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 24 Jul 2017
How does breaking detailed balance accelerate convergence to equilibrium?
Jack, R
Tuesday 18th July 2017 - 11:00 to 11:40
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 19 Jul 2017
Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo and exact approximation
Vihola, M
Thursday 6th July 2017 - 13:30 to 14:15
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 24 Jul 2017
Inference in generative models using the Wasserstein distance
Robert, C
Friday 7th July 2017 - 11:45 to 12:30
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 24 Jul 2017
Inference with approximate likelihoods
Ogden, H
Monday 3rd July 2017 - 15:30 to 16:15
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jul 2017
Kinetic energy choice in Hamiltonian/hybrid Monte Carlo
Livingstone, S
Wednesday 5th July 2017 - 11:00 to 11:45
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Fri 21 Jul 2017
Langevin MCMC: theory and methods
Moulines, E
Friday 7th July 2017 - 09:00 to 09:45
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 24 Jul 2017
Measuring Sample Discrepancy with Diffusions
Duncan, A
Tuesday 18th July 2017 - 15:40 to 16:20
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Wed 19 Jul 2017
Mobile Apps and Machine Learning for Improving Healthcare
Heller, K
Thursday 6th July 2017 - 09:45 to 10:30
Collection: Scalable inference; statistical, algorithmic, computational aspects
Institution: Isaac Newton Institute for Mathematical Sciences
Created: Mon 24 Jul 2017