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  2. Log probability - Wikipedia

    en.wikipedia.org/wiki/Log_probability

    (The conversion to log form is expensive, but is only incurred once.) Multiplication arises from calculating the probability that multiple independent events occur: the probability that all independent events of interest occur is the product of all these events' probabilities. Accuracy.

  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. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    The curve shows the estimated probability of passing an exam (binary dependent variable) versus hours studying (scalar independent variable). See § Example for worked details. In statistics, the logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables.

  5. Log-logistic distribution - Wikipedia

    en.wikipedia.org/wiki/Log-logistic_distribution

    In probability and statistics, the log-logistic distribution (known as the Fisk distribution in economics) is a continuous probability distribution for a non-negative random variable. It is used in survival analysis as a parametric model for events whose rate increases initially and decreases later, as, for example, mortality rate from cancer ...

  6. Propensity score matching - Wikipedia

    en.wikipedia.org/wiki/Propensity_score_matching

    Obtain an estimation for the propensity score: predicted probability p or the log odds, log[p/(1 − p)]. 2. Match each participant to one or more nonparticipants on propensity score, using one of these methods: Nearest neighbor matching

  7. 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

  8. Log5 - Wikipedia

    en.wikipedia.org/wiki/Log5

    The name Log5 is due to Bill James [1] but the method of using odds ratios in this way dates back much farther. This is in effect a logistic rating model and is therefore equivalent to the Bradley–Terry model used for paired comparisons, the Elo rating system used in chess and the Rasch model used in the analysis of categorical data. [2]

  9. 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.