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