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.

  • mu (float) – mean value of normal distribution
  • sigma (float) – standard deviation of normal distribution