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  2. Log probability - Wikipedia

    en.wikipedia.org/wiki/Log_probability

    In probability theory and computer science, a log probability is simply a logarithm of a probability. [1] The use of log probabilities means representing probabilities on a logarithmic scale ( − ∞ , 0 ] {\displaystyle (-\infty ,0]} , instead of the standard [ 0 , 1 ] {\displaystyle [0,1]} unit interval .

  3. Logit - Wikipedia

    en.wikipedia.org/wiki/Logit

    If p is a probability, then p/(1 − p) is the corresponding odds; the logit of the probability is the logarithm of the odds, i.e.: ⁡ = ⁡ = ⁡ ⁡ = ⁡ = ⁡ (). The base of the logarithm function used is of little importance in the present article, as long as it is greater than 1, but the natural logarithm with base e is the one most often used.

  4. Log-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Log-normal_distribution

    A probability distribution is not uniquely determined by the moments E[X n] = e nμ + ⁠ 1 / 2 ⁠ n 2 σ 2 for n ≥ 1. That is, there exist other distributions with the same set of moments. [4] In fact, there is a whole family of distributions with the same moments as the log-normal distribution. [citation needed]

  5. Logit-normal distribution - Wikipedia

    en.wikipedia.org/wiki/Logit-normal_distribution

    In probability theory, a logit-normal distribution is a probability distribution of a random variable whose logit has a normal distribution.If Y is a random variable with a normal distribution, and t is the standard logistic function, then X = t(Y) has a logit-normal distribution; likewise, if X is logit-normally distributed, then Y = logit(X)= log (X/(1-X)) is normally distributed.

  6. Binary regression - Wikipedia

    en.wikipedia.org/wiki/Binary_regression

    The simplest direct probabilistic model is the logit model, which models the log-odds as a linear function of the explanatory variable or variables. The logit model is "simplest" in the sense of generalized linear models (GLIM): the log-odds are the natural parameter for the exponential family of the Bernoulli distribution, and thus it is the simplest to use for computations.

  7. Odds ratio - Wikipedia

    en.wikipedia.org/wiki/Odds_ratio

    The log odds ratio shown here is based on the odds for the event occurring in group B relative to the odds for the event occurring in group A. Thus, when the probability of X occurring in group B is greater than the probability of X occurring in group A, the odds ratio is greater than 1, and the log odds ratio is greater than 0.

  8. ‘Why do we want to bastardize our game?’: MLB players and ...

    www.aol.com/sports/why-want-bastardize-game-mlb...

    Various figures from around the MLB have criticized commissioner Rob Manfred’s suggestion of a Golden At-Bat rule, which would allow managers to send anyone they like to the plate once per game.

  9. Logarithmic distribution - Wikipedia

    en.wikipedia.org/wiki/Logarithmic_distribution

    A Poisson compounded with Log(p)-distributed random variables has a negative binomial distribution. In other words, if N is a random variable with a Poisson distribution , and X i , i = 1, 2, 3, ... is an infinite sequence of independent identically distributed random variables each having a Log( p ) distribution, then