Running Maud

Maud provides the following commands:

• maud sample [PATH TO MAUD INPUT]

• maud simulate [PATH TO MAUD INPUT]

• maud variational [PATH TO MAUD INPUT]

• maud predict [PATH TO MAUD SAMPLE OUTPUT]

The documentation for each of these commands is as follows:

sample

Generate MCMC samples given a user input directory.

This function creates a new directory in output_dir with a name starting with “maud_output”. It first copies the directory at data_path into the new this directory at new_dir/user_input, then runs the running_stan.sample function to write samples in new_dir/samples. Finally it prints the results of cmdstanpy’s diagnose and summary methods.

Run the sample function as a click command.

sample [OPTIONS] [DATA_PATH]

Options

--output_dir <output_dir>

Where to save Maud’s output

Arguments

DATA_PATH

Optional argument

simulate

Generate draws from the initial values.

simulate [OPTIONS] [DATA_PATH]

Options

--output_dir <output_dir>

Where to save the output

-n <n>

Number of simulations

Arguments

DATA_PATH

Optional argument

variational

Generate MCMC samples given a user input directory.

This function creates a new directory in output_dir with a name starting with “maud_output”. It first copies the directory at data_path into the new this directory at new_dir/user_input, then runs the running_stan.sample function to write samples in new_dir/samples. Finally it prints the results of cmdstanpy’s diagnose and summary methods.

variational [OPTIONS] [DATA_PATH]

Options

--output_dir <output_dir>

Where to save Maud’s output

Arguments

DATA_PATH

Optional argument

predict

Generate MCMC samples given a Maud output folder at train_path.

This function creates a new directory in output_dir with a name starting with “maud-predict-output”. It first copies the testing directory at train_path into the new this directory at new_dir/user_input, then runs the running_stan.predict_out_of_sample function to write samples in new_dir/oos_samples.

The trained output is stored in the new_dir/trained_samples folder along with the user input required to generate the trained samples.

predict [OPTIONS] DATA_PATH

Arguments

DATA_PATH

Required argument