class_docstr: Integrated GP-Lower Confidence Bound acquisition function
  
  :param model: GPyOpt class of model
  :param space: GPyOpt class of domain
  :param optimizer: optimizer of the acquisition. Should be a GPyOpt optimizer
  :param cost_withGradients: function
  :param exploration_weight: positive parameter to control exploration / exploitation
  
  .. Note:: allows to compute the Improvement per unit of cost
class_docstr_tok: ["Integrated","GP-Lower","Confidence","Bound","acquisition","function",":","param","model",":","GPyOpt","class","of","model",":","param","space",":","GPyOpt","class","of","domain",":","param","optimizer",":","optimizer","of","the","acquisition",".","Should","be","a","GPyOpt","optimizer",":","param","cost_withGradients",":","function",":","param","exploration_weight",":","positive","parameter","to","control","exploration","\/","exploitation","..","Note",":",":","allows","to","compute","the","Improvement","per","unit","of","cost"]
code: def __init__(
      self,
      model,
      space,
      optimizer=None,
      cost_withGradients=None,
      exploration_weight=2,
  ):
      super(AcquisitionLCB_MCMC, self).__init__(
          model,
          space,
          optimizer,
          cost_withGradients,
          exploration_weight,
      )
      assert (
          self.model.MCMC_sampler
      ), "Samples from the hyper-parameters are needed to compute the integrated GP-LCB"
  
imports: from .LCB import AcquisitionLCB
label_desc: Use image node_id 1 to create a new AcquisitionLCB_MCMC object from inherited base classes: AcquisitionLCB with example: obj = AcquisitionLCB_MCMC(model, space, optimizer, cost_withGradients, exploration_weight)