# (OLD) Configuration options

`filtering`

section

`filtering`

sectionThe `filtering`

section configures the settings for the inference algorithm. The below example shows the settings for some typical default settings, where the model is calibrated to the weekly incident deaths and weekly incident confirmed cases for each subpop. Statistics, hierarchical_stats_geo, and priors each have scenario names (e.g., `sum_deaths,`

`local_var_hierarchy,`

and `local_var_prior,`

respectively).

`filtering`

settings

`filtering`

settingsWith inference model runs, the number of simulations `nsimulations`

refers to the number of final model simulations that will be produced. The `filtering$simulations_per_slot`

setting refers to the number of iterative simulations that will be run in order to produce a single final simulation (i.e., number of simulations in a single MCMC chain).

`filtering::statistics`

`filtering::statistics`

The statistics specified here are used to calibrate the model to empirical data. If multiple statistics are specified, this inference is performed jointly and they are weighted in the likelihood according to the number of data points and the variance of the proposal distribution.

`filtering::hierarchical_stats_geo`

`filtering::hierarchical_stats_geo`

The hierarchical settings specified here are used to group the inference of certain parameters together (similar to inference in "hierarchical" or "fixed/group effects" models). For example, users may desire to group all counties in a given state because they are geograhically proximate and impacted by the same statewide policies. The effect should be to make these inferred parameters follow a normal distribution and to observe shrinkage among the variance in these grouped estimates.

`filtering::priors`

`filtering::priors`

It is now possible to specify prior values for inferred parameters. This will have the effect of speeding up model convergence.

## Ground truth data

## Likelihood function

## Fitting parameters

## Ground truth data

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