point_process_analysis

point_process_analysis(
    data,
    threshold=None,
    total_years=None,
    sampler='DEMCz',
    iterations=3000,
    output_length=10000,
    credible_level=0.9,
    seed=12345,
    exceedance_probabilities=None,
    thinning_interval=-1,
)

Bayesian point-process (peaks-over-threshold) frequency analysis.

Fit a peaks-over-threshold point-process model to data with a Bayesian MCMC analysis.

Parameters

Name	Type	Description	Default
data	array_like	Observations to fit.	required
threshold	float	Peaks-over-threshold value; the model default cascade applies when omitted.	None
total_years	float	Total record length in years; the model default cascade applies when omitted.	None
sampler	('DEMCz', 'DEMCzs', 'ARWMH', 'NUTS')	MCMC sampler.	"DEMCz"
iterations	int	Number of post-warmup MCMC iterations.	3000
output_length	int	Number of posterior samples used to build the credible band.	10000
credible_level	float	Credible-interval width.	0.90
seed	int	PRNG seed for the sampler.	12345
exceedance_probabilities	array_like of float	Exceedance probabilities at which to tabulate the curve; when None, the default ordinates are used.	None
thinning_interval	int	MCMC thinning interval; -1 keeps the sampler’s own default.	-1

Returns

Name	Type	Description
	dict	The same shape as univariate_analysis: parameters, mode_curve, mean_curve, lower_ci, upper_ci, aic, bic, dic, rmse.

See Also

univariate_analysis, mixture_analysis