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9.6.2: Probability Formula

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    51695
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    The probability distribution \(p(A_i)\) we want has been derived by others. It is a function of the dual variable \(\beta\):

    \(p(A_i) = 2^{-\alpha} 2^{-\beta g(A_i)} \tag{9.12}\)

    which implies

    \(\log_2 \Big(\dfrac{1}{p(A_i)} \Big) = \alpha + \beta g(A_i) \tag{9.13}\)

    where \(\alpha\) is a convenient abbreviation\(^2\) for this function of \(\beta\):

    \(\alpha = \log_2 \Big( \displaystyle \sum_{i} 2^{-\beta g(A_i)} \Big) \tag{9.14}\)

    Note that this formula for \(\alpha\) guarantees that the \(p(A_i)\) from Equation 9.12 add up to 1 as required by Equation 9.8.

    If \(\beta\) is known, the function \(\alpha\) and the probabilities \(p(A_i)\) can be found and, if desired, the entropy \(S\) and the constraint variable \(G\). In fact, if \(S\) is needed, it can be calculated directly, without evaluating the \(p(A_i)\)—this is helpful if there are dozens or more probabilities to deal with. This short-cut is found by multiplying Equation 9.13 by \(p(A_i)\), and summing over \(i\). The left-hand side is \(S\) and the right-hand side simplifies because \(\alpha\) and \(\beta\) are independent of \(i\). The result is

    \(S = \alpha + \beta G \tag{9.15}\)

    where \(S\), \(\alpha\), and \(G\) are all functions of \(\beta\).


    \(^2\)The function \(\alpha(\beta)\) is related to the partition function \(Z(\beta)\) of statistical physics: \(Z = 2^{\alpha}\) or \(\alpha = \log_2 Z\).


    This page titled 9.6.2: Probability Formula is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Paul Penfield, Jr. (MIT OpenCourseWare) via source content that was edited to the style and standards of the LibreTexts platform.