class_docstr: Evidence log(Z) and posterior weight summation for a Nested Sampling tree.
class_docstr_tok: ["Evidence","log","(","Z",")","and","posterior","weight","summation","for","a","Nested","Sampling","tree","."]
docstr: Initialise.
  
  Parameters
  ----------
  random: bool
      if False, use mean estimator for volume shrinkage
      if True, draw a random sample
docstr_tok: ["Initialise",".","Parameters","--","--","--","--","--","random",":","bool","if","False",",","use","mean","estimator","for","volume","shrinkage","if","True",",","draw","a","random","sample"]
code: def __init__(self, random=False):
      """Initialise.
  
      Parameters
      ----------
      random: bool
          if False, use mean estimator for volume shrinkage
          if True, draw a random sample
      """
      self.reset()
      self.random = random
  
imports: import numpy
  from numpy import log, log1p, exp, logaddexp
  import math
  import sys
  from .utils import resample_equal
  from .ordertest import UniformOrderAccumulator
label_desc: Use image node_id 1 to create a new SingleCounter object from inherited base classes: object with example: obj = SingleCounter(random)