pyhctsa.operations.distribution.fit_mle¶

pyhctsa.operations.distribution.fit_mle(y, fit_what='gaussian')¶

Maximum likelihood distribution fit to data.

Fits a specified probability distribution to the data using maximum likelihood estimation (MLE) and returns the fitted parameters.

Parameters:¶

y : array-like¶

Input time series or data vector

fit_what : {'gaussian', 'uniform', 'geometric'}, optional¶

Distribution type to fit:

• ’gaussian’: Normal distribution (returns mean and std)

• ’uniform’: Uniform distribution (returns bounds a and b)

• ’geometric’: Geometric distribution (returns p parameter)

Default is ‘gaussian’

Returns:¶

For ‘gaussian’:

dict with keys:

• ’mean’: location parameter

• ’std’: scale parameter

For ‘uniform’:

dict with keys:

• ’a’: lower bound

• ’b’: upper bound

For ‘geometric’:

float: success probability p

Return type:¶

Union[Dict[str, float], float]