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SBC Intro

Martin Modrák

2021/08/24 (updated: 2022-01-26)

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This work was supported by ELIXIR CZ research infrastructure project (MEYS Grant No: LM2018131) including access to computing and storage facilities.

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Context

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Context

  • SBC can be a useful tool to check you implemented your model correctly.
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Context

  • SBC can be a useful tool to check you implemented your model correctly.

  • It just one of tools to validate your model in a Bayesian workflow

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Context

  • SBC can be a useful tool to check you implemented your model correctly.

  • It just one of tools to validate your model in a Bayesian workflow

  • SBC can be run even before you collect data

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Context

  • SBC can be a useful tool to check you implemented your model correctly.

  • It just one of tools to validate your model in a Bayesian workflow

  • SBC can be run even before you collect data

  • The goal of this tutorial is to show you that with the SBC package, there is little cost to including (some form of) SBC in your everyday modelling workflow.

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Context

  • SBC can be a useful tool to check you implemented your model correctly.

  • It just one of tools to validate your model in a Bayesian workflow

  • SBC can be run even before you collect data

  • The goal of this tutorial is to show you that with the SBC package, there is little cost to including (some form of) SBC in your everyday modelling workflow.

  • In this tutorial, we'll let you use SBC to find and diagnose a bug.

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Context

  • SBC can be a useful tool to check you implemented your model correctly.

  • It just one of tools to validate your model in a Bayesian workflow

  • SBC can be run even before you collect data

  • The goal of this tutorial is to show you that with the SBC package, there is little cost to including (some form of) SBC in your everyday modelling workflow.

  • In this tutorial, we'll let you use SBC to find and diagnose a bug.

  • We will show toy problems, but have applied it to non-toy problems as well.

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Two types of problems with Stan model

  1. Bug in model
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Two types of problems with Stan model

  1. Bug in model

  2. Data - model mismatch

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Two types of problems with Stan model

  1. Bug in model

  2. Data - model mismatch

We will focus on 1.

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Result of SBC (1)

SBC fails: There is a mismatch between our model, algorithm and simulator.

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Result of SBC (1)

SBC fails: There is a mismatch between our model, algorithm and simulator.

     The mismatch can be anywhere!

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Result of SBC (2)

SBC passes: To the precision availabe with the given number of simulations, our model, algorithm and simulator are consistent.

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Result of SBC (2)

SBC passes: To the precision availabe with the given number of simulations, our model, algorithm and simulator are consistent.

    For a specific sense of consistent. Nothing more, nothing less.

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This work was supported by ELIXIR CZ research infrastructure project (MEYS Grant No: LM2018131) including access to computing and storage facilities.

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