class_docstr: Exponential Distribution
  ----
  This class contains methods relating to the Exponential distribution for time series.
docstr: Log PDF for Exponential prior
  
  Parameters
  ----------
  mu : float
      Latent variable for which the prior is being formed over
  
  Returns
  ----------
  - log(p(mu))
docstr_tok: ["Log","PDF","for","Exponential","prior","Parameters","--","--","--","--","--","mu",":","float","Latent","variable","for","which","the","prior","is","being","formed","over","Returns","--","--","--","--","--","-","log","(","p","(","mu",")",")"]
code: def logpdf(self, mu):
      """
      Log PDF for Exponential prior
  
      Parameters
      ----------
      mu : float
          Latent variable for which the prior is being formed over
  
      Returns
      ----------
      - log(p(mu))
      """
      if self.transform is not None:
          mu = self.transform(mu)
      return ss.expon.logpdf(mu, self.lmd0)
  
imports: import numpy
  import scipy.stats
  import scipy.special
  from .family import Family
  from .flat import Flat
  from .normal import Normal
  from .gas_recursions import gas_recursion_exponential_orderone, gas_recursion_exponential_ordertwo
  from .gas_recursions import gasx_recursion_exponential_orderone, gasx_recursion_exponential_ordertwo
  from .gas_recursions import gas_llev_recursion_exponential_orderone, gas_llev_recursion_exponential_ordertwo
  from .gas_recursions import gas_llt_recursion_exponential_orderone, gas_llt_recursion_exponential_ordertwo
  from .gas_recursions import gas_reg_recursion_exponential_orderone, gas_reg_recursion_exponential_ordertwo
label_desc: Use image node_id 7 for calling the Exponential obj's underlying member method code with example usage: obj.logpdf(mu) and returns: ss