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@@ -42,33 +42,34 @@ usage: main.py [-h] [--group {corpus,child}] [--chains CHAINS] [--samples SAMPLE
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main model described throughout the notes.
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optional arguments:
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- -h, --help show this help message and exit
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- --group {corpus,child}
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- --chains CHAINS
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- --samples SAMPLES
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- --validation VALIDATION
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- --output OUTPUT
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- ```
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+-h, --help show this help message and exit
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+--group {corpus,child}
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+--chains CHAINS
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+--samples SAMPLES
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+--validation VALIDATION
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+--output OUTPUT
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+```
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+
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+The ``--group`` parameter controls the primary level of the hierarchical model. The model indeed assumes that confusion rates (i.e. confusion probabilities) vary across corpora (``corpus``) or children (``child``).
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- The ``--group`` parameter controls the primary level of the hierarchical model. The model indeed assumes that confusion rates (i.e. confusion probabilities) vary across corpora (``corpus``) or children (``child``).
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+The ``--chains`` parameter sets the amount of MCMC chains, and ``--samples`` controls the amount of MCMC samples, warmup excluded.
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- The ``--chains`` parameter sets the amount of MCMC chains, and ``--samples`` controls the amount of MCMC samples, warmup excluded.
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+The ``--validation`` parameter sets the amount of annotation clips used for validation rather than training. Set it to 0 in order to use as much data for training as possible.
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- The ``--validation`` parameter sets the amount of annotation clips used for validation rather than training. Set it to 0 in order to use as much data for training as possible.
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+The ``--output`` parameter controls the output destination. Training data will be saved to ``output/samples/data_{output}.pickle`` and the MCMC samples are saved as ``output/samples/fit_{output}.parquet``
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- The ``--output`` parameter controls the output destination. Training data will be saved to ``output/samples/data_{output}.pickle`` and the MCMC samples are saved as ``output/samples/fit_{output}.parquet``
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- ### Confusion probabilities
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+### Confusion probabilities
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- The marginal posterior distribution of the confusion matrix is shown below:
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+The marginal posterior distribution of the confusion matrix is shown below:
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- 
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+
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- ### Speech distribution
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+### Speech distribution
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- Speech distributions used to generate the simulated "null-hypothesis" corpora are fitted against the training data using Gamma distributions. The code used to fit these distributions is found in ``code/models/speech_distribution``.
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+Speech distributions used to generate the simulated "null-hypothesis" corpora are fitted against the training data using Gamma distributions. The code used to fit these distributions is found in ``code/models/speech_distribution``.
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- The match between the training data and the Gamma parametrization can be observed in various plots in ``output``. See below for the Key child:
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+The match between the training data and the Gamma parametrization can be observed in various plots in ``output``. See below for the Key child:
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- 
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+
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