Multidimensional

preliz.multidimensional

preliz.multidimensional.dirichlet_mode(mode, mass=None, bound=0.01, plot=None, plot_kwargs=None, ax=None)

Elicitate a Dirichlet distribution with a given mode and mass.

Computes a Dirichlet distribution where the marginals have the specified mode and mass and their masses lie within the range mode ± bound (Adapted from Evans et al.[1]).

Parameters:

• mode (list) – Mode of the Dirichlet distribution.

• mass (float) – Probability mass between within mode +- bounds. Defaults to None, which results in the value of rcParams[“stats.ci_prob”] being used.

• bound (float) – Defines upper and lower bounds for the mass as mode +- bound. Defaults to 0.01.

• plot (bool) – Whether to plot the distribution. Defaults to True.

• plot_kwargs (dict) – Dictionary passed to the method plot_pdf().

• ax (matplotlib axes)

Returns:

• ax (matplotlib axes)

• dist (Preliz Dirichlet distribution.) – Dirichlet distribution with fitted parameters alpha for the given mass and intervals.

References

[1]

Michael Evans, Irwin Guttman, and Peiying Li. Prior elicitation, assessment and inference with a Dirichlet prior. Entropy, 2017. URL: https://www.mdpi.com/1099-4300/19/10/564, doi:10.3390/e19100564.