Page 154 - Modern Spatiotemporal Geostatistics
P. 154

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        THE     CHOICE OF A          SPATIOTEMPORAL
        ESTIMATE


            "Natural  science does not simply  describe and  explain  nature,  it is
            part of the interplay  between nature and scientists."  W.  Heisenberg


        Versatility    of  the  BME Approach

        At  the  integration  (or  posterior)  stage of the  BME  analysis, estimates must be
        determined  at  the  space/time  mapping  points.  BME  is a  versatile  approach
        that  allows for  a variety  of  possibilities  regarding  the  choice of  the  appropri-
        ate  spatiotemporal  estimate  at  the  integration  stage.  As a  matter  of  fact, an
        interesting  interpretation  of  the  integration  stage  is  obtained  in  the  context
        of  scientific  demonstration,  as the  latter  is  understood  in  natural sciences.
        Scientific  demonstration—in  the  wide  sense—stands  for  any  experiential  ev-
        idence  that  has  a  large  measure  of  cogency  or  suasive  power  relative  to  a
        predictive  map.  In  other  words,  scientific  demonstration  may  be associated
        with  specificatory data that  lead to  a BME  estimate with  high  posterior  prob-
        ability,  or  some  other  desirable  feature.  Indeed,  since  the  posterior  pdf  is
        rigorously  determined  through  the  BME  analysis, a  large  number  of  options
        become available, depending on the  physical, economical, and other character-
        istics  of  the  application  considered.  In  other  words,  the  BME  maps  obtained
        are case-specific.
            Many people will agree that a cogent  choice of an estimate  is the map that
        maximizes  the  posterior  pdf.  This  choice leads  to  a BMEmode  map which is
        described  in the  following section; a case study  involving  an extensive  porosity
        data  set  is discussed in  the  section  entitled  "The  West  Lyons  Porosity  Field"
        (p.  143).  Other  choices include  maps that  optimize  the stochastic  expectation
        (with  respect  to  the  posterior  pdf)  of  a  function  of  the  natural  variable  of
        interest.  Typical  examples are the conditional  mean estimate (which  minimizes

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