# pastas.stats.metrics.bic#

bic(obs=None, sim=None, res=None, missing='drop', nparam=1)[source]#

Compute the Bayesian Information Criterium (BIC).

Parameters
• obs (pandas.Series) – Series with the observed values.

• sim (pandas.Series) – Series with the simulated values.

• res (pandas.Series) – Series with the residual values. If time series for the residuals are provided, the sim and obs arguments are ignored.

• nparam (int, optional) – number of calibrated parameters.

• missing (str, optional) – string with the rule to deal with missing values. Only “drop” is supported now.

Notes

The Bayesian Information Criterium (BIC) Akaike (1979) is computed as follows:

$\text{BIC} = -2 log(L) + n_{param} * log(N)$

where $$n_{param}$$ is the number of calibration parameters.