def updateMedShockProcess(self):
        '''
        Constructs discrete distributions of medical preference shocks for each
        period in the cycle.  Distributions are saved as attribute MedShkDstn,
        which is added to time_vary.

        Parameters
        ----------
        None

        Returns
        -------
        None
        '''
        MedShkDstn = [] # empty list for medical shock distribution each period
        for t in range(self.T_cycle):
            MedShkAvgNow  = self.MedShkAvg[t] # get shock distribution parameters
            MedShkStdNow  = self.MedShkStd[t]
            MedShkDstnNow = approxLognormal(mu=np.log(MedShkAvgNow)-0.5*MedShkStdNow**2,\
                            sigma=MedShkStdNow,N=self.MedShkCount, tail_N=self.MedShkCountTail,
                            tail_bound=[0,0.9])
            MedShkDstnNow = addDiscreteOutcomeConstantMean(MedShkDstnNow,0.0,0.0,sort=True) # add point at zero with no probability
            MedShkDstn.append(MedShkDstnNow)
        self.MedShkDstn = MedShkDstn
        self.addToTimeVary('MedShkDstn')
Constructs discrete distributions of medical preference shocks for each
        period in the cycle.  Distributions are saved as attribute MedShkDstn,
        which is added to time_vary.

        Parameters
        ----------
        None

        Returns
        -------
        None