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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 , instead of the standard unit interval. Since the probabilities of independent events multiply, and logarithms convert multiplication to ...
considered as a function of , is the likelihood function, given the outcome of the random variable . Sometimes the probability of "the value of for the parameter value " is written as P(X = x | θ) or P(X = x; θ). The likelihood is the probability that a particular outcome is observed when the true value of the parameter is , equivalent to the ...
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A graph showing the minimum value of the sample log odds ratio statistic that must be observed to be deemed significant at the 0.05 level, for a given sample size. The three lines correspond to different settings of the marginal probabilities in the 2×2 contingency table (the row and column marginal probabilities are equal in this graph).
Like approximate entropy (ApEn), Sample entropy (SampEn) is a measure of complexity. [1] But it does not include self-similar patterns as ApEn does. For a given embedding dimension, tolerance and number of data points, SampEn is the negative natural logarithm of the probability that if two sets of simultaneous data points of length have distance < then two sets of simultaneous data points of ...
This is justified by considering the central limit theorem in the log domain (sometimes called Gibrat's law). The log-normal distribution is the maximum entropy probability distribution for a random variate X —for which the mean and variance of ln (X) are specified.
Likelihood-ratio test. In statistics, the likelihood-ratio test is a hypothesis test that involves comparing the goodness of fit of two competing statistical models, typically one found by maximization over the entire parameter space and another found after imposing some constraint, based on the ratio of their likelihoods.