Parameter recovery
Simulate data for a range of different known parameter settings. Estimate parameters from simulated data. See if estimated and known parameters match. Tests whether the model+inference method can properly reconstruct parameter values of the gneerative process.
Can be implemented using fix() to set parameter values and predict() to generate data. Then condition the model on the generated data. Can be parallelised across parameter settings. Will be computationally heavy, so storing temporary results and allowing for resuming later would be useful.
Arguments: Turing model; specifications of which variables are parameters and which are data; full set of parameter settings to be used; settings for parallelisation and temporary storage; settings for inference method (e.g., arguments to sample() or vi()), number of repetitions.
Output: Full set of samples across parameters alongside the known values used for generating behaviour, across all parameter settings, across repetitions.
Parameter recovery
Simulate data for a range of different known parameter settings. Estimate parameters from simulated data. See if estimated and known parameters match. Tests whether the model+inference method can properly reconstruct parameter values of the gneerative process.
Can be implemented using fix() to set parameter values and predict() to generate data. Then condition the model on the generated data. Can be parallelised across parameter settings. Will be computationally heavy, so storing temporary results and allowing for resuming later would be useful.
Arguments: Turing model; specifications of which variables are parameters and which are data; full set of parameter settings to be used; settings for parallelisation and temporary storage; settings for inference method (e.g., arguments to sample() or vi()), number of repetitions.
Output: Full set of samples across parameters alongside the known values used for generating behaviour, across all parameter settings, across repetitions.