mmd_hypothesis_test#
- bayesflow.diagnostics.mmd_hypothesis_test(mmd_null: ndarray, mmd_observed: float = None, alpha_level: float = 0.05, null_color: str | tuple = '#132a70', observed_color: str | tuple = 'red', alpha_color: str | tuple = 'orange', truncate_v_lines_at_kde: bool = False, x_min: float = None, x_max: float = None, bw_factor: float = 1.5)[source]#
- Parameters:
- mmd_nullnp.ndarray
The samples from the MMD sampling distribution under the null hypothesis “the model is well-specified”
- mmd_observedfloat
The observed MMD value
- alpha_levelfloat, optional, default: 0.05
The rejection probability (type I error)
- null_colorstr or tuple, optional, default: (0.16407, 0.020171, 0.577478)
The color of the H0 sampling distribution
- observed_colorstr or tuple, optional, default: “red”
The color of the observed MMD
- alpha_colorstr or tuple, optional, default: “orange”
The color of the rejection area
- truncate_v_lines_at_kde: bool, optional, default: False
true: cut off the vlines at the kde false: continue kde lines across the plot
- x_minfloat, optional, default: None
The lower x-axis limit
- x_maxfloat, optional, default: None
The upper x-axis limit
- bw_factorfloat, optional, default: 1.5
bandwidth (aka. smoothing parameter) of the kernel density estimate
- Returns:
- fplt.Figure - the figure instance for optional saving