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Program (2)

Day 1
1.1 Simulators and inference1.1 Simulators and inference
1.1 Simulators and inference

Álvaro Tejero-Cantero

🎚️Introduction to sbi: simulation & inference

1.2 Practical: from ABC to SBI1.2
1.2 Practical: from ABC to SBI

Álvaro Tejero-Cantero

Notebook (GitHub)

1.3 Invited lecture: SBI in neuroscience1.3
1.3 Invited lecture: SBI in neuroscience

Pedro J. Gonçalves

PDF slides

Day 2
2.1 (Conditional) density estimation2.1 (Conditional) density estimation
2.1 (Conditional) density estimation

Michael Deistler

Notebook (GitHub)

2.2 Sequential neural posterion estimation explained2.2 Sequential neural posterion estimation explained
2.2 Sequential neural posterion estimation explained

Michael Deistler

Notebook (GitHub)

2.3 SNLE and SNRE2.3
2.3 SNLE and SNRE

David Greenberg

Notebook (GitHub)

Day 3
3.1 Benchmarking sbi3.1
3.1 Benchmarking sbi

Jan-Matthis Lückmann

PDF slides

3.2 Introduction to the sbi toolkit3.2 Introduction to the sbi toolkit
3.2 Introduction to the sbi toolkit

Jan Bölts

Notebook (GitHub)

3.3 Troubleshooting sbi3.3 Troubleshooting sbi
3.3 Troubleshooting sbi

Jan Bölts

Notebook (GitHub)

3.4 Bayesian workflow3.4
3.4 Bayesian workflow

Jan Bölts

Notebook (GitHub)

Extras (day 3)
3.5 Bayesian model comparison3.5 Bayesian model comparison
3.5 Bayesian model comparison

Jan Bölts

PDF slides

3.6 Bring your own simulator and apply sbi to your problem!3.6 Bring your own simulator and apply sbi to your problem!
3.6 Bring your own simulator and apply sbi to your problem!

Participant led!

N/A

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Cluster ML in Science  ⊂

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