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  2. Observed information - Wikipedia

    en.wikipedia.org/wiki/Observed_information

    In statistics, the observed information, or observed Fisher information, is the negative of the second derivative (the Hessian matrix) of the "log-likelihood" (the logarithm of the likelihood function). It is a sample-based version of the Fisher information.

  3. Fisher information - Wikipedia

    en.wikipedia.org/wiki/Fisher_information

    In mathematical statistics, the Fisher information is a way of measuring the amount of information that an observable random variable X carries about an unknown parameter θ of a distribution that models X. Formally, it is the variance of the score, or the expected value of the observed information.

  4. Realization (probability) - Wikipedia

    en.wikipedia.org/wiki/Realization_(probability)

    In probability and statistics, a realization, observation, or observed value, of a random variable is the value that is actually observed (what actually happened). The random variable itself is the process dictating how the observation comes about.

  5. Latent and observable variables - Wikipedia

    en.wikipedia.org/wiki/Latent_and_observable...

    Variables that have no correlation cannot result in a latent construct based on the common factor model. [5] The "Big Five personality traits" have been inferred using factor analysis. extraversion [6] spatial ability [6] wisdom “Two of the more predominant means of assessing wisdom include wisdom-related performance and latent variable ...

  6. Maximum likelihood estimation - Wikipedia

    en.wikipedia.org/wiki/Maximum_likelihood_estimation

    In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data.This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable.

  7. Likelihood principle - Wikipedia

    en.wikipedia.org/wiki/Likelihood_principle

    Some classical significance tests are not based on the likelihood. The following are a simple and more complicated example of those, using a commonly cited example called the optional stopping problem. Example 1 – simple version. Suppose I tell you that I tossed a coin 12 times and in the process observed 3 heads.

  8. Glossary of probability and statistics - Wikipedia

    en.wikipedia.org/wiki/Glossary_of_probability...

    Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...

  9. Statistical inference - Wikipedia

    en.wikipedia.org/wiki/Statistical_inference

    Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling.Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model.

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