# 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