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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
-
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