class_docstr: Integrated Maximum Probability of Improvement 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 jitter: positive value to make the acquisition more explorative
  
  .. Note:: allows to compute the Improvement per unit of cost
code: def __init__(
      self,
      model,
      space,
      optimizer=None,
      cost_withGradients=None,
      jitter=0.01,
  ):
      super(AcquisitionMPI_MCMC, self).__init__(
          model, space, optimizer, cost_withGradients, jitter
      )
  
      assert (
          self.model.MCMC_sampler
      ), "Samples from the hyper-parameters are needed to compute the integrated EI"
  
imports: from .MPI import AcquisitionMPI
  from ..util.general import get_quantiles
label_desc: Use image node_id 1 to create a new AcquisitionMPI_MCMC object from inherited base classes: AcquisitionMPI with example: obj = AcquisitionMPI_MCMC(model, space, optimizer, cost_withGradients, jitter)