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The method I described in BAYES.TXT is intended as a tool for evaluating ho
w
consistent various data are with a given set of hypotheses. It is not an
evaluation tool for the data itself. Data inputs must be accurate and
reliable, otherwise you are likely to get garbage.
For example, take President Reagan's remarks in Dec 1985 about, "Well, I
don't suppose we can wait for some alien race to come down and threaten
us...." Since this remark was widely reported, we can take it as both
accurate (it reflects what Reagan said) and reliable (checking it from severa
l
sources gives the same answer). The issue then is consistency with our
hypotheses (from BAYES.TXT).
Hypothesis 1: US gov't contact, no disinformation. Reagan's remarks are
very inconsistent (20% correlation).
Hypothesis 2: US gov't contact, some disinformation. Reagans remarks are
very consistent (80% correlation).
Hypothesis 3: US gov't contact, all disinformation. Reagan's remarks are
fairly consistent (60% correlation).
Hypothesis 4: No US gov't contact, no disinformation. Reagan's remarks are
fairly consistent (60% correlation).
Hypothesis 5: No US gov't contact, some disinformation. Reagan's remarks
are somewhat consistent (40% correlation).
Hypothesis 6: No US gov't contact, all disinformation. Reagan's remarks
very inconsistent (20% correlation).
Let's apply these judgements to our model (I picked the initial values for
the sake of argument, not because I necessarily endorse them).
Hypotheses Initial Datum Product Revised
Value One Value
Hyp 1 10% 20% 2% 3.45%
Hyp 2 30% 80% 24% 41.38%
Hyp 3 25% 60% 15% 25.86%
Hyp 4 20% 60% 2% 20.69%
Hyp 5 10% 40% 4% 6.90%
Hyp 6 5% 20% 1% 1.72%
TOTAL 100% 0.58