code: def params_of_mean(value=array([-.005, 1.]), tau=.1, rate=4.):
      
  
      def logp(value, tau, rate):
          if value[1] > 0 and value[1] + value[0] * 110 > 0:
              return normal_like(value[0], 0., tau) + \
                  exponential_like(value[1], rate)
          else:
              return -Inf
  
      def random(tau, rate):
          val = zeros(2)
          val[0] = rnormal(0., tau)
          val[1] = rexponential(rate)
          while val[1] < 0 or val[1] + val[0] * 110 <= 0:
              val[0] = rnormal(0., tau)
              val[1] = rexponential(rate)
          return val
docs: Intercept and slope of rate stochastic of poisson distribution
      Rate stochastic must be positive for t in [0,T]
  
      p(intercept, slope|tau,rate) =
      N(slope|0,tau) Exp(intercept|rate) 1(intercept>0) 1(intercept + slope * T>0)