class_docstr: The trend component that features the polynomial trending,
  providing one building block for the dynamic linear model.
  It decribes a latent polynomial trending in the time series data.
  
  Args:
      degree: the degree of the polynomial. 0: constant; 1: linear...
      discount: the discount factor
      name: the name of the trend component
      w: the value to set the prior covariance. Default to a diagonal
         matrix with 1e7 on the diagonal.
  
  Examples:
      >>>  # create a constant trend
      >>> ctrend = trend(degree = 1, name = 'Const', discount = 0.99)
      >>>  # change the ctrend to have covariance with diagonals are 2 and state 1
      >>> ctrend.createCovPrior(cov = 2)
      >>> ctrend.createMeanPrior(mean = 1)
  
  Attributes:
      d: the dimension of the latent states of the polynomial trend
      componentType: the type of the component, in this case, 'trend'
      name: the name of the trend component, to be supplied by user
            used in modeling and result extraction
      discount: the discount factor for this component. Details please refer
                to the @kalmanFilter
      evaluation: the evaluation matrix for this component
      transition: the transition matrix for this component
      covPrior: the prior guess of the covariance matrix of the latent states
      meanPrior: the prior guess of the latent states
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docstr: Create the prior latent state
  
          
docstr_tok: ["Create","the","prior","latent","state"]
code: def createMeanPrior(self, mean=0):
      """Create the prior latent state"""
      self.meanPrior = np.matrix(np.ones((self.d, 1))) * mean
  
file_path: pydlm/pydlm/modeler/trends.py
imports: import numpy
  from .component import component
  import pydlm.base.tools
label_desc: Use image node_id 5 for calling the trend obj's underlying member method code with example usage: obj.createMeanPrior(mean) without return types