A woman looks at health data

New Approaches to Bayesian Data Science

Tackling Challenges from the Health Sciences

This EPSRC project brings together an exceptional team of investigators with a world-leading track record in fundamental data science and applications to health and biomedical sciences.


The health sciences have seen an explosion in the amount of data collected at both individual and population levels. This data can be varied, including genetic information, health records, data on activity levels obtained from wearable devices, and image data from scans. There is huge potential for improved diagnoses, timely interventions and more effective treatments if we can fully extract understanding from this data. Example applications included real-time monitoring of patients, developing personalised treatment, or real-time monitoring and decision-making for epidemics. However the data science challenges in extracting these insights are vast.

Features of these challenges include the need to make inferences about and decisions for individuals from within a population, and the need to synthesise information from disparate data sources and data types. Whilst we have substantial data collected at a population level, the amount of information on any given individual may be still be limited. Appropriately quantifying uncertainty is crucial for making decisions, with the optimal decision often being driven by the probability of relatively rare events (e.g. extreme reaction to a drug). We need model-based approaches to data science that can leverage scientific understanding, but we need the statistical analyses to be robust to unavoidable inadequacies of these models. Underpinning many of these applications is the requirement to develop new understanding, and this differs from a focus on making predictions that it is most common among current statistical or machine learning methods.

Bayesian data science provides a natural framework for tackling these challenges. Bayesian methods are model-based, can appropriately quantify and propagate uncertainty, and through hierarchical models are able to use population-level information when making inferences about individuals. Repeated application of Bayes theorem gives a natural paradigm for synthesizing information across multiple data sources. However, current Bayesian data science methods are not feasible for many modern, big-data, applications in the health sciences. Bayesian methods require integrating over uncertainty. Such high-dimensional integration carries a substantial computational overhead when compared to alternative, often optimization-based, data science methods. So while the motivation for Bayesian analysis is clear, this computational overhead means that, currently, implementing Bayesian approaches is often not feasible.

This programme of research will develop the new approaches to Bayesian data science that are needed both within the health sciences and more widely. It builds on recent breakthroughs in Monte Carlo integration methods that show great promise for being efficient for large data; and on new paradigms for Bayesian-like updates that are suitable for complex models and which focus modelling effort just on the aspects of these models that are most important. It will address key research challenges in the health sciences - directly developing new insights and understanding for these.

Our People

We bring together world-leading expertise in the fields of data and health science.

View the people involved
A stethoscope lies on a desk

Our Research

Methodological Themes

  • Scalable Monte Carlo Methods

    Monte Carlo methods have enabled Bayesian statistics to be applied routinely. This theme will develop novel version of algorithms such as MCMC that have excellent computational properties and that are efficient in big-data scenarios and can take advantage of the power of parallel computing.

  • Fusing Information from Disparate Sources

    We will develop principled and practicable Bayesian approaches that enable information to be combined across different data sources types, such as health records, genotype information and, perhaps, image data from a fMRI scan.

  • Robust Bayesian Methods

    Bayesian methods necessarily make modelling assumptions which will not hold exactly. We will develop novel Bayesian-type approaches that acknowledge, account for, and, hence, are robust to such model error.

Motivating Challenges

  • Monitoring of Epidemics

    Prompt public health responses to outbreaks of epidemics relies on real-time monitoring and prediction of their spread.

  • Genomics and Phenotyping

    There is currently a step change in the number of very large cohort studies that are collecting and analysing detailed genetic information as well as rich phenotypic data.

  • Personalized Medicine

    Being able to customize medical care to each individual patient has huge potential for improving patient care through earlier diagnoses and timely and more appropriate interventions and medication.

Working in Partnership

This project involves leading researchers in data science and its application to health from the universities of Bristol, Cambridge, Lancaster, Oxford and Warwick.