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8 The Importance of Ontological Structure: Why Validation by ‘Fit-to-Data’...  165



                              is
                    0),  0),  0)),  model  has  In  for   vdata)  vector  all  model  <   (continued)

                1&  1,  1,  1,  0,  1,  0,  case,  being  model  the  typically  entries.  caters  incremented  epsilon  a  is  whether  vdata.This  the  of
                DD  DD model  DD model  DD model  this  In  setting.  setting parameter  where  cases  data:  the  vdata) ‘DD‘,  is  data  the  FALSE  of  full  be  usually  term  ‘noise’  be  to  BIC  accuracy,  parameter):  y,  epsilong <  near.enough,  ceq likelihood,  saying  model)  to  enough)  probability  all)est.likelihood  length(ceq)

                matrix(c(sum(ifelse(vdata  0),  1,  &  0  DD  &  1  DD  &  0  DD  2)  D  parameter  given  a  for  the  with  model  the  the  store  to  meq  as output  same  2, apply(model,  and  model  the  between  normally  then  would  Gaussian  the  and  AIC  the  in  k  an allowing alternative,  another  as counts  function(x,  <  fabs(x-










              require(vcd)    m<  1,  DD model sum(ifelse(vdata sum(ifelse(vdata sum(ifelse(vdata  ncol  2,  D nrow  Kappa(m)  outputs  of  of replication  each  amatrix Firstwebuild  the produced    <  meq  equality  Strict  meq unrealistic;  models, mathematical  requires  but  issue  this  An accordingly.  similarly  (which  near.enough  D eps








                the           distribution  a  for  column  a  of  is  Log  to
              of  comparing  of  has  and  for  models).  parameter  data.  parameter  estimated.  easier
              degree  probability  one  and  probability  computations  parameters  of  the  to  prediction  be

              the  by  the    stochastic  vdata  linear  the  of  set  fit  better  likelihood  model’s  be  only  can  they
              measures  model  the  of  is  model  of  using  as  as  (such  function  given  a  For  a  maximum  the  can  as  distributions


              that  and  estimate  an  chance  the  length  the  vdata  computed  available  algorithms  a  is  data.  suggests  for  of  likelihoods  reported
              statistic  data  the  with  by  where  as  in  row  are  R,  observed  likelihood  search  distribution  known  of

              a               case  rows  the  typically  in  likelihood  function),  sometimes  case
              is  between  agreement  occurring  of  to  and,  model-fitting  the  could  the
              kappa           the  for  number  are  the  given  higher  a  Unless  a  (as  are  the  in

              Cohen’s  agreement  observed  agreement  available  same  corresponding  Likelihoods  distributions,  specific  Essentially,  model  values,  Calibration  values.  known  likelihoods  compute


                              are  the  with  inputs
              kappa           metrics  matrix  and

              Cohen’s         Various  a  now  evaluated  Likelihood
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