Priors¶

Module for important calculations involving the prior. For example,

when scaling the Latin Hypercube samples to the appropriate prior range
when calculating the posterior if the emulator is connected with an MCMC sampler

priors.all_entities(dict_params)[source]¶

Generate all the priors once we have specified them.

Param

dict_params (dict) - a list containing the description for each parameter and each description (dictionary) contains the following information:

Returns

record (list) - a list containing the prior for each parameter, that is, each element contains the following information:

priors.entity(dictionary)[source]¶

Generates the entity of each parameter by using scipy.stats function.

Param

dictionary (dict) - a dictionary containing information for each parameter, that is,

Returns

dist (dict) - the distribution generated using scipy

priors.log_prod_pdf(desc: dict, parameters: dict) → float[source]¶

Calculate the log-product for a set of parameters given the priors

Param: desc (dict) - dictionary of parameters
Param: parameters (np.ndarray) - an array of parameters
Returns: log_sum (float) - the log-product of when the pdf of each parameter is multiplied with another