Prior Definition Module

Functions to define prior distribution of parameters

InferenceWorkflow.prior.flat_prior(low, up, random)

Generate a flat prior distribution for a given parameter,

Parameters:

• low (float) – lower bound of this flat distribution.

• up (float) – upper bound of this flat distribution.

• random (float) – random number generated to do inference, this is follow the

• UltraNest (definition of baysian workflow of)

• cube[i] (here default to be)

Returns:

ppf of this distribution function

Return type:

ppf (float)

InferenceWorkflow.prior.normal_Prior(center, width, random)

Generate a normal prior distribution for a given parameter,

Parameters:

• center (float) – center value of this gaussian distribution.

• width (float) – width of this gaussian distribution, this is the 1-sigma width.

• random (float) – random number generated to do inference, this is follow the

• UltraNest (definition of baysian workflow of)

• cube[i] (here default to be)

Returns:

ppf of this distribution function

Return type:

ppf (float)