## Philosophy Lexicon of Arguments | |||

Bayesianism: perceives probability as the degree of a belief. See also subjective probability, objective probability, chance, likelihood._____________ Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||

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Books on Amazon |
I 161 Bayesian Theorem/Formula/Frith: P(A I X) = P(X I A) * P(A) P(X) Says to what extent we should update our knowledge about A with respect to the new information. --- I 162 P(A): Prior knowledge: 1% of women over 40 get breast cancer. New information: good test proving breast cancer. P(XI A): 80% of all women with breast cancer are tested positive in the test. P(X ~ A): 9.6% of women without breast cancer are tested positive in the test. P(A I X): what is the proportion of those with a positive test result who actually have breast cancer? Common mistake: most consider the proportion of women who actually have breast cancer to be very high. --- I 163 Solution: E.g. 10 000 women are examined Group 1: 100 with cancer Group 2: 9,900 without cancer. P(A): 1%. After the examination one has 4 groups: A: 80 women with cancer and with a positive test result B: 20 with cancer but with a false negative result In A, the 80% with a correct-positive result are: p(X I A) C: 950 without cancer but with a false-positive result D: 8950 without cancer and with a negative result. Question: What is the percentage of women with a positive outcome who actually have cancer? Solution: Group A is divided by the sum of A and C. This is 7.8% N.B.: more than 90% of positively tested women have no cancer. Although the test is a good test, the Bayesian theorem says that the new information is not particularly helpful. --- I 164 Bayesian Theorem/Frith: the Bayesian theorem tells us precisely how much new information should influence our ideas about the world. Definition ideal Bayesian observer/Frith: the ideal Bayesian observer always uses information in an optimal way. Problem: we can classify information badly when it comes to rare events and large numbers. Brain: although we as persons are not ideal observers, there is much evidence that our brain is an ideal observer. --- I 165 For example, if an object is very rare, an observer needs more information to believe that it is actually there. Therefore, in the case of bombs, we are not an ideal observer. Brain: has the task of combining the information from the different sensory channels. _____________ Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. |
Frith I Chris Frith Wie unser Gehirn die Welt erschafft Heidelberg 2013 |

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Ed. Martin Schulz, access date 2017-06-24