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  2. Log probability - Wikipedia

    en.wikipedia.org/wiki/Log_probability

    Log probabilities are thus practical for computations, and have an intuitive interpretation in terms of information theory: the negative expected value of the log probabilities is the information entropy of an event. Similarly, likelihoods are often transformed to the log scale, and the corresponding log-likelihood can be interpreted as the ...

  3. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    The log-likelihood function being plotted is used in the computation of the score (the gradient of the log-likelihood) and Fisher information (the curvature of the log-likelihood). Thus, the graph has a direct interpretation in the context of maximum likelihood estimation and likelihood-ratio tests.

  4. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    For logistic regression, the measure of goodness-of-fit is the likelihood function L, or its logarithm, the log-likelihood ℓ. The likelihood function L is analogous to the ε 2 {\displaystyle \varepsilon ^{2}} in the linear regression case, except that the likelihood is maximized rather than minimized.

  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. G-test - Wikipedia

    en.wikipedia.org/wiki/G-test

    We can derive the value of the G-test from the log-likelihood ratio test where the underlying model is a multinomial model. Suppose we had a sample x = ( x 1 , … , x m ) {\textstyle x=(x_{1},\ldots ,x_{m})} where each x i {\textstyle x_{i}} is the number of times that an object of type i {\textstyle i} was observed.

  7. Multinomial logistic regression - Wikipedia

    en.wikipedia.org/wiki/Multinomial_logistic...

    The formulation of binary logistic regression as a log-linear model can be directly extended to multi-way regression. That is, we model the logarithm of the probability of seeing a given output using the linear predictor as well as an additional normalization factor, the logarithm of the partition function:

  8. Where to watch NFL Thanksgiving games: Kickoff times, TV ...

    www.aol.com/where-watch-nfl-thanksgiving-games...

    NFL fans are gearing up for one of the best holiday traditions in sports. The league is set to kick off its playoff push with three Thanksgiving games, featuring several teams vying for playoff spots.

  9. 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.