ICIC Data Analysis Workshop 2016


Principled statistical methods for researchers



Imperial Centre for Inference and Cosmology (ICIC), Imperial College, South Kensington, London.  


Starts: 2 p.m. 5 September 2016.

Ends:   4 p.m. 8 September 2016. 


We will run a 4-day course/workshop on statistical methods and tools for data analysis, aimed at PhD students, postdocs and any researchers interested in understanding Bayesian statistics and numerical techniques of data analysis. The course plan is to combine lectures with hands-on computational work in the afternoons. It will concentrate on setting down firm foundations of principled Bayesian data analysis, but a feature of the workshop will be a substantial element of hands-on classes where participants will learn how to apply the ideas in practice. It will be hosted by the Imperial Centre for Inference and Cosmology (ICIC) at Imperial College. 


Most researchers will at some point be required to perform some form of data analysis.  This may be anything from simple line-fitting, through parameter estimation, to complex and computationally-demanding sampling for model selection on large datasets. Anecdotal evidence suggests that many researchers are not well prepared for this, often doing the right thing incorrectly, or picking up an inappropriate statistical tool.   The purpose of this course is to provide understanding of principled data analysis, and experience of applying appropriate methods to data.


We expect all participants to bring their own laptop, and to do a simple computational exercise in advance (in whatever language suits) to ensure they have appropriate software in place before the workshop starts.   


The workshop has sponsorship from STFC's Education, Training and Careers Committee, and also a generous donation from Winton Capital.  


STFC-funded students will have accommodation, meals, refreshments and social activities paid for, and can also claim travel from STFC.  Other participants may need to pay accommodation costs and a £75 course fee - see the registration page for more details of eligibility for cost reductions.


We have reserved rooms in Imperial College Student Accommodation, in Beit Hall, within easy walking distance of the workshop.


Social events

There will be a drinks reception on the roof terrace on Monday evening, and a barbecue at Princes Gate on Wednesday.  Tuesday evening will be free to allow participants to explore London. 


Day 1 (Monday 5 September 2016). Clore Lecture Theatre, Huxley Building

  • From 1.30 p.m. Registration.
  • Start of Workshop 2 p.m.
  • Bayesian Foundations: 
  • What is probability?
  • The Laws of Probability and Bayes’ Theorem
  • Priors
  • Parameter inference
  • Marginalization
  • Confidence intervals, credibility intervals
  • Problem class: Simple problems
  • Tutorial: day summary
  • Talk by Dr Geraint Harker (Winton Capital and UCL Astrophysics)
  • End: 5.30 p.m.
  • Drinks reception. Roof Terrace, Level 8, Blackett Lab.


Day 2 (Tues 6 September 2016).  Skempton Building Lecture Theatre 201

  • Bayesian Computation: Parameter Estimation and Sampling
  • Grid-based methods
  • Markov Chain Monte Carlo
  • Metropolis-Hastings algorithm
  • Convergence tests – Rubin-Gelman
  • Hands on: MCMC code from scratch.  Cosmology from the Supernova Hubble Diagram.
  • Day summary
  • End: 5 p.m.
  • Evening: free


Day 3 (Weds 7 September 2016) Clore Lecture Theatre, Huxley Building

  • Gibbs Sampling
  • Hamiltonian Monte Carlo
  • Day summary
  • End: 5 p.m.
  • 6 p.m. Workshop Barbecue. 58 Princes Gate.


Day 4 (Thurs 8 September 2016) Clore Lecture Theatre, Huxley Building

  • Why not p-values and reduced chisquared?
  • Model Comparison with Bayesian Evidence
  • Hands on: Bayesian evidence: the Savage-Dickey Density Ratio
  • Bayesian Hierarchical Models
  • Workshop summary
  • 4 p.m. End of Workshop


At the end of the Workshop, the participants should be able to (non-exhaustive list):

  • Express stochastic problems in terms of fundamental probability and Bayes’ theorem.
  • Demonstrate by application to real data understanding of probability, inference, priors, posteriors, marginalisation, parameter estimation, hypothesis testing, model selection, sampling.
  • Code and apply a simple MCMC program to physical data.
  • Formulate model selection problems in a principled statistical framework, and be capable of executing some methods of solution.


Prof Alan Heavens, Prof Andrew Jaffe, Dr Jonathan Pritchard, Dr Daniel Mortlock (ICIC Physics and Mathematics), Dr Elena Sellentin (University of Geneva/ICIC Physics)


Point of contact: Professor Alan Heavens, Director, Imperial Centre for Inference and Cosmology, Blackett Laboratory, Prince Consort Road, London SW7 2AZ. Email a.heavens@ null imperial.ac.uk Tel. 0207 594 2930, or Louise Hayward (l.hayward@ null imperial.ac.uk) 0207 594 7679.


Registration is now closed. Please contact Louise Hayward for adminstrative matters l.hayward@ null imperial.ac.uk.


Lecturers and Demonstrators

Prof Alan Heavens (ICIC Physics)

Prof Andrew Jaffe (ICIC Physics)

Dr Daniel Mortlock (ICIC Physics and Mathematics)

Dr Jonathan Pritchard (ICIC Physics)

Dr Elena Sellentin (University of Geneva and ICIC Physics)

List of Participants 

A list of registered participants will be available later.

Code of conduct

The meeting has a code of conduct: Meeting Code of Conduct.pdf


STFC claim form

For STFC- and self-funded students, travel and extra subsistence can be claimed using STFC Claim form.pdf. Rerturn to Studentships, STFC, Polaris House, North Star Avenue, Swindon SN2 1SZ

Practical info

Local map 

Local restaurant suggestions



The Huxley Building is very close to the Royal Albert Hall in South Kensington.  You enter Huxley (No. 13 on the map) from the Queen's Gate road side.  Nearest tube stops are South Kensington and Gloucester Road (10 mins walk), and there are buses which pass close by - see the map and the TfL website for details ( http://www.tfl.gov.uk/ ).  You may find the Journey Planner facility useful.  Note that it is worth buying a pay-as-you-go Oyster card if you are going to use the system at all - the fares are much cheaper than buying individual tickets. You can also use a (UK only) contactless payment card, which has the same fares as Oyster.

From South Kensington Underground station: if it's raining, when you come through the barriers, you can turn right below ground and take the long tunnel from the station, which emerges pretty much at the bottom of the main map (attached).  Otherwise, it's preferable to go straight ahead and head up for the daylight, exit to street level immediately, turning right to get into Thurloe St (this is mostly pedestrianised now) - it's a more pleasant walk above ground past the museums.




Course materials will appear here.


Preliminary Exercise:


Please do this BEFORE the workshop!


Instructions and background: Preliminary Exercise 2016.pdf

Supernova data file: jla_mub.txt

Covariance matrix: jla_mub_covmatrix.txt


Workshop handouts:


Programme and logistics: Welcome Pack a.pdf

Day 1 exercises: HandsOn Day 1 2016.pdf

Day 1 solutions: HandsOn Day 1 solutions 2016.pdf


Day 1 lecture - Alan Heavens: ICIC Workshop Lecture 1 2016 final.pdf

Day 1 lecture - Jonathan Pritchard: Marginalisation_JRP.pdf


Day 2: SN MCMC exercise instructions (updated from paper copy): SN MCMC project 2016.pdf

Day 2: Gelman-Rubin formula: GelmanRubin_fromSAS_STAT_usersguide.pdf

Day 2 lecture - Daniel Mortlock: Metropolis MCMC notes metropolis_DM.pdf

Day 2 lecture - Alan Heavens: CLT and the Lighthouse problem (also a better answer to Day 1 problem) CLT and Lighthouse Problem.pdf


Day 3 lecture - Andrew Jaffe: Gibbs, HMC, Linear Models Gibbs_HMC_GLM_AJ.pdf

Day 3 lecture - Elena Sellentin: Model comparison ModelSelection_ES.pdf


Day 4 - Jonathan Pritchard: Nested sampling NestedSampling_JRP.pdf

Day 4 - Elena Sellentin: Frequentist and Bayesian NoiseVsSignal_ES.pdf

Day 4 - Alan Heavens: Bayesian Hierarchical Models BHM_AH.pdf


Day 4 - Alan Heavens: SDDR slide SDDR slide.pdf


Day 4 - Roberto Trotta: Public Engagement Lunch PE_Lunch RT.pdf


Python code


Alan's python 3 code for the 2-parameter case: SNcodePython.txt



Inverse covariance matrix: Inverse covariance.txt


Claim ID: ViYeNeywdWpD6HHy
Claim Passcode: caZ9XYJEX3qTjKcHClaim ID: ViYeNeywdWpD6HHyClaim



Updated programme: ICIC 2016 Programme.pdf