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Frequently asked questions

This page contains a list of frequently asked questions about the EpiAware package. If you have a question that is not answered here, please open a discussion on the GitHub repository.

    Pluto notebooks

    In some of the showcase examples in EpiAware/docs/src/showcase we use Pluto.jl notebooks for the underlying computation. As well as reading the code blocks and output of the notebooks in this documentation, you can also run these notebooks by cloning EpiAware and running the notebooks with Pluto.jl (for further details see developer notes).

    It should be noted that Pluto.jl notebooks are reactive, meaning that they re-run downstream code after changes with downstreaming determined by a tree of dependent code blocks. This is different from the standard Julia REPL, and some other notebook formats (e.g. .ipynb). In Pluto each code block is a single lines of code or encapsulated by let ... end and begin ... end. The difference between let ... end blocks and begin ... end blocks are that the let ... end type of code block only adds the final output/return value of the block to scope, like an anonymous function, whereas begin ... end executes each line and adds defined variables to scope.

    For installation instructions and more information and documentation on Pluto.jl see the Pluto.jl documentation.

    Manipulating EpiAware model specifications

    Modular model construction

    One of the key features of EpiAware is the ability to specify models as components of a larger model. This is useful for specifying models that are shared across multiple EpiProblems or for specifying models that are used in multiple methods. You can see an examples of this approach in our showcases.

    Remaking models

    An alternative to modular model construction is to remake models with different parameters. This can be useful for comparing models with different parameters or for comparing models with different priors. Whilst we don't have a built in function for this, we recommend the Accessors.jl package for this purpose. For examples of how to use this package see the documentation.

    Working with Turing.jl models

    DynamicPPL.jl

    Whilst Turing.jl is the front end of the Turing.jl ecosystem, it is not the only package that can be used to work with Turing.jl models. DynamicPPL.jl is the part of the ecosytem that deals with defining, running, and manipulating models.

    Conditioning and deconditioning models

    DynamicPPL supports the condition (alased with |) to fix values as known observations in the model (i.e fixing values on the left hand side of ~ definitions). This is useful for fixing parameters to known values or for conditioning the model on data. The decondition function can be used to remove these conditions. Internally this is what apply_method(::EpiProblem, ...) does to condition the user supplied EpiProblem to data. See more here.

    Fixing and unfixing models

    Similarly to conditioning and deconditioning models, DynamicPPL supports fixing and unfixing models via the fix and unfix functions. Fixing is essentially saying that variables are constants (i.e replacing the right hand side of ~ with a value and changing the ~ to a =). A common use of this would be to simplify a prespecified model, for example to make the variance of a random walk be known versus estimated from the data. We also use this functionality in apply_method(::EpiProblem, ...) to allow users to simplify EpiProblems on the fly. See more here.

    Tools for working with MCMCChain objects

    MCMCChain.jl

    MCMCChain.jl is the package from which MCMCChains is imported. It provides a number of useful functions for working with MCMCChain objects. These include functions for summarising, plotting, and manipulating chains. Below is a list of some of the most useful functions.

    • plot: Plots trace and density plots for each parameter in the chain object.
    • histogram: Plots histograms for each parameter in the chain object by chain.
    • get: Accesses the values of a parameter/s in the chain object.
    • DataFrames.DataFrame converts a chain into a wide format DataFrame.
    • describe: Prints the summary statistics of the chain object.

    There are many more functions available in the MCMCChain.jl package. For a full list of functions, see the documentation.

    Arviz.jl

    An alternative to MCMCChain.jl is the ArviZ.jl package. ArviZ.jl is a Julia meta-package for exploratory analysis of Bayesian models. It is part of the ArviZ project, which also includes a related Python package.

    ArviZ.jl uses a InferenceData object to store the results of a Bayesian analysis. This object can be created from a MCMCChain object using the from_mcmcchains function. The InferenceData object can then be used to create a range of plots and summaries of the model. This is particularly useful as it allows you to specify the indexes of your parameters (for example you could use dates for time parameters).

    In addition to this useful functionality from_mcmcchains can also be used to combine posterior predictions with prior predictions, prior information and the log likelihood of the model (see here for an example of this). This unlocks a range of useful diagnostics and plots that can be used to assess the model.

    There is a lot of functionality in ArviZ.jl and it is worth exploring the documentation to see what is available.