Estimation Models

class regmmd.models.estimation.Beta(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.BetaA(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.BetaB(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.Binomial(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.Gamma(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.GammaRate(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.GammaShape(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.Gaussian(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.GaussianLoc(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.GaussianScale(par_v=None, par_c=None, random_state=None)

score(x)

Evaluates to the gradient of the log likelihood with respect to the parameters at the values x.

Parameters:

x (ndarray of shape (n_samples, n_features), the points to be evaluated)

update(par_v)

Update the model with new parameters

Parameters:

par_v (float, variable parameters)

class regmmd.models.estimation.Poisson(par_v=None, par_c=None, random_state=None)