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# System prepended metadata

title: brainstorming a 1-day workshop on SBI

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# brainstorming a 1-day workshop on SBI

## Venue & Connection Details

Friday, April 30, 10am 

Topic: Brainstorming a one-day SBI workshop
Time: Apr 30, 2021 10:00 AM Amsterdam, Berlin, Rome, Stockholm, Vienna

Join Zoom Meeting
https://zoom.us/j/99276028397?pwd=WmZ3aEZobXB1TXpzUW0zcTZjaUtpQT09

Meeting ID: 992 7602 8397
Passcode: 156017
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+496971049922,,99276028397#,,,,*156017# Germany
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Find your local number: https://zoom.us/u/asVi9HV5H

Participants:
David Greenberg, Michael Deistler, Daniela Huppenkothen, Jan Bolts, Alvaro Tejero-Cantero, Peter Steinbach, Sebastian Starke

## SBI users

- Daniela Huppenkothen
    - bayesian inference of time series
    - institute for astrononmy something in NL
    - data from xray telescopes when observing very bright objects
    - use SBI to assess systematic effects of instruments
    - involved in proposals wrt to surrogate models in astronomy/cosmology
    - example:
        - https://arxiv.org/abs/2104.03278
        - simulator can be coded easily (write the function in 30min), but inversion is hard if not impossible to assess
        - simulated very short time series
        - Q: in neuroscience models are typically mechanistic -> what is the inference goal
            - phyiscal process generates time seris 
            - black hole and a star: gas falling into black hole
            - induces quasi periodic process -> need to measure this
            - non-linear effects scale with brightness
            - generate time series -> goes through simulator -> list of observed photons
            - 2 simulators: one for physical process and one for measure
    - Jan@Daniela: for hierarchical inference have a look a 
        - https://arxiv.org/abs/2102.06477
        - or in general, learn a synthetic likelihood and then you can arrange the likelihood terms as you need them and run MCMC. 
            
- Sebastian (statistician by training with a blind eye on Bayesian theory)
    - 2 interests: 
        - where does SBI internals fit into statistics?
        - work with radiation therapy dept at local uni clinic
            - working on risk models for patient induced treatment
            - trying to predict how long a patient will survive given a CT image
            - idea for SBI: feed patient data into SBI and retrieve CT image
            - idea for SBI: provide CT and obtain expected survival time
                - David: conditional density estimation (partially contained in SBI)
                - used simple CNNs with mediocre results
                
    - SBI designed for (conditional) density estimation
        - maybe invest first half of a workshop day

- Peter Steinbach
    - working on simulation of accelerator beamline (produces beam profile images)
        - inverting the simulation is the goal
        - image highly discontinuous
        - also small-angle scattering: reconstruct from an image the shape that produced (?) the scattering image
    - time series estimation (down the line, currently working on static)


## learner profiles

- Phd students from the Helmholtz AI network and affiliated groups
- Helmholtz AI consultants
- Scientists at Uni Tübingen
- MLColab consultants

- DH: mostly have 2 crowds
    - beginners that like to learn SBI as a concept and `sbi` as a tool
    - exchange with more advnaced users like DH ;-) in the line of "how to do hierarchical inference with SBI"
    

- what is the scope of this?
    - DH: more didactical to learn the tool `sbi` or more a workshop to exchange wishes/experiences
    - PS: not discussed yet -> my interest is learning the tool and get grasp of theory (depends on available time budget of all)
    - DG: time budget is limit,  would contribute a talk, supervise interactive session
        - interested to see how SBI is used and applied on new domains, learn about strenghts and weaknesses of methods used
        - workshop might open the room for collaboration, long run publications?
    - ATC: long term effort to come up with tutorials more closer to domains
        - should we aim to produce papers with this? publications form this workshop might happen naturally
        - balance consulting and research
        - joint event between Tubingen and Helmholtz? 
        - tool for (conditional) density estimation? (community would appreciate this)
            - JB: pyknos?
            - ATC: yes, but maybe not too amendable to users
            - ATC: would appreciate feedbac
       
- FORMAT: at least one day. Maybe split in two half days
    - split into "introduction" and "work on your data"
    - we can help with infrastructure, getting software up and running, beta-testing, participant communication
    - JB: good ideas so far -> 1-2 day workshop sounds fine
    - MD: would be up for the introduction, have lecture ready for master students in Tuebingen
    
- need about 3 months for preparation -> target second half of September

- how large should audience be?
    - PS: suggest 20 people in first session
        - AT agrees to use small number as a trial run -> scaling up later not too much effort
    - maybe do a video-recording for the introduction part
        - flipped class-room: after watching recording, learners can get in touch with mentors to ask questions
        - DH: maybe learners will not prepare properly if asked to do something in advance 

- further communication among this group will be done by mail


## content wishes

- Introduction to the theory on how posteriors are estimated
    - which role do the neural networks play here (what exactly is optimised given the simulation data)
    - how are the networks used in posterior sampling
    - which network architectures are used (what are MAFs, NSFs etc...)
    - how do the concepts SNPE, SNLE, SNRE differ and when should we use which
    - is MCMC involved?
  
- Introduction to the SBI terms
    - what are "rounds of inference"
    - what exactly is amortization
    - simple interface vs flexible interface -> use cases
    - leakage of the prior
- Evaluation - how do I know I am right
    - introduce the sbi benchmark (for offline perusal)
    - how to interpret a posterior (marginals, conditionals)
    - model criticism
    - model misspecification, model comparison (-> Jan? just describe the limitations of sbi at this point)
- Advanced topics (we should discuss if this is to be part of the WS, or part 2)
    - Hierarchical inference https://arxiv.org/abs/2102.06477 
    - SBI on manifolds (cf. Cranmer group paper)
    - Move 'advanced' topics to 2022 workshop, merge broader interest ones above.

- 
## AOB

- targeted workshop for more advanced users and method developers -> targeted at 2022
    - https://www.lorentzcenter.nl/lorentz-escience-competition.html 
