bivariate_analysis

bivariate_analysis(
    marginal_x_family,
    marginal_x_data,
    marginal_x_parameters,
    marginal_y_family,
    marginal_y_data,
    marginal_y_parameters,
    xy_x,
    xy_y,
    copula='Normal',
    estimation_method='InferenceFromMargins',
    sampler='DEMCz',
    iterations=3000,
    output_length=10000,
    credible_level=0.9,
    seed=12345,
    number_of_chains=4,
    thinning_interval=-1,
)

Bivariate (copula) joint-exceedance frequency analysis over two fixed marginals.

Wraps the shared C++ BivariateAnalysis.

Parameters

Name	Type	Description	Default
marginal_x_family	str	Distribution family name of the X marginal.	required
marginal_x_data	array_like	Observations for the X marginal.	required
marginal_x_parameters	array_like of float	Fixed parameter values of the X marginal.	required
marginal_y_family	str	Distribution family name of the Y marginal.	required
marginal_y_data	array_like	Observations for the Y marginal.	required
marginal_y_parameters	array_like of float	Fixed parameter values of the Y marginal.	required
xy_x	array_like of float	X values of the joint-exceedance ordinate grid.	required
xy_y	array_like of float	Y values of the joint-exceedance ordinate grid.	required
copula	str	Bivariate copula name.	"Normal"
estimation_method	str	Copula estimation method.	"InferenceFromMargins"
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
number_of_chains	int	Number of MCMC chains.	4
thinning_interval	int	MCMC thinning interval; -1 keeps the sampler’s own default.	-1

Returns

Name	Type	Description
	dict	The AND-joint-exceedance mode/mean curve and credible band over the (xy_x, xy_y) ordinate grid.