lssm_process

         return a one dimensional LSSM model evenly time series
     state equation       : x(t) = 'AR[p]_process'
     observation equation : y(t) = C*x(t)+noise    , noise = N(0,lssmsig)

         timing tools

         lssm_process,time,rate,ar1=ar1,ar2=ar2                      $
                   ,relaxation=relaxation,period=period              $
                   ,lssmc=lssmc,lssmsig=lssmsig

         time : time array of evenly binned lightcurve

         none

         a1         : if set, work with AR[1] process
         a2         : if set, work with AR[2] process
         relaxation : parameter value of the AR[1&2] process ; damping
                      rate (in  bins)
         period     : parameter value of the AR[2] process ; period
         sig        : parameter value of the AR[1&2] process ; variance
                      of the dynamical white noise ( if not set, equal 1.0 )
         c          : parameter value of the AR[1&2] process ;
                      influence in the mean (if not set, equal 0)
         lssmc      : design matrix, C, who maps the unobservable
                      dynamics to the observation. (y(t) = C*x(t) + noise)
         lssmsig    : variance of the observable white noise
                      component ( noise = N(0,sig)

         rate       : count rate array of the LSSM lightcurve

         ar_rate    : count rate array of the unobservable AR[p] lightcurve

         none

         none

         AR stationary conditions

         uses ar1_process, ar2_process procedures, and the
         idl-randomn function

         lssm_process,time,rate,/ar2,relaxation=100,period=50,   $
                  lssmc=2,lssmsig=3.5,ar_rate=ar_rate

         Version 1.0, 1999.01.13, Sara Benlloch (IAAT)
                                 ([email protected])

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