Priors¶
pypbl.priors¶
Priors for item weights.

class
pypbl.priors.
Exponential
(mu=1)¶ Exponential prior. This prior is particularly useful if you deterministically know the sign of the weight, and have a guess for the value of the weight. The mean may be negative.
Parameters: mu (float) – mean value of exponential distribution

class
pypbl.priors.
Flat
¶ Flat prior. This is useful when you are completely agnostic to the weights on a particular weight. You may wish to remove this parameter from the analysis instead.

class
pypbl.priors.
Normal
(mu=0, sigma=1)¶ Normal prior. This prior is useful if you have a good guess for what the weight should be, and an understanding of how much you expect to differ from that guess.
Parameters:  mu (float) – mean value of normal distribution
 sigma (float) – standard deviation of normal distribution