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  2. Weibull distribution - Wikipedia

    en.wikipedia.org/wiki/Weibull_distribution

    In probability theory and statistics, the Weibull distribution / ˈ w aɪ b ʊ l / is a continuous probability distribution.It models a broad range of random variables, largely in the nature of a time to failure or time between events.

  3. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the ...

  4. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    Stated in terms of odds, Bayes' rule states that the posterior odds of two alternatives, ⁠ ⁠ and ⁠ ⁠, given an event ⁠ ⁠, is the prior odds, times the likelihood ratio. As an equation: O ( A 1 : A 2 ∣ B ) = O ( A 1 : A 2 ) ⋅ Λ ( A 1 : A 2 ∣ B ) . {\displaystyle O(A_{1}:A_{2}\mid B)=O(A_{1}:A_{2})\cdot \Lambda (A_{1}:A_{2}\mid ...

  5. Survival function - Wikipedia

    en.wikipedia.org/wiki/Survival_function

    For example, in survival function 4, more than 50% of the subjects survive longer than the observation period of 10 months. Median survival greater than 10 months. The survival function is one of several ways to describe and display survival data. Another useful way to display data is a graph showing the distribution of survival times of subjects.

  6. Wilks' theorem - Wikipedia

    en.wikipedia.org/wiki/Wilks'_theorem

    However, in another test of a factor with 15 levels, they found a reasonable match to () – 4 more degrees of freedom than the 14 that one would get from a naïve (inappropriate) application of Wilks’ theorem, and the simulated p-value was several times the naïve ().

  7. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    [4] [5] Their importance is partly due to the central limit theorem. It states that, under some conditions, the average of many samples (observations) of a random variable with finite mean and variance is itself a random variable—whose distribution converges to a normal distribution as the number of samples increases.

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  9. Total variation distance of probability measures - Wikipedia

    en.wikipedia.org/wiki/Total_variation_distance...

    Total variation distance is half the absolute area between the two curves: Half the shaded area above. In probability theory, the total variation distance is a statistical distance between probability distributions, and is sometimes called the statistical distance, statistical difference or variational distance.