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  1. Bayesian probability

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    Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted

  2. Statistics

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    terms of the design of surveys and experiments. See glossary of probability and statistics. When census data cannot be collected, statisticians collect data

  3. Bayesian inference

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    update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially

  4. Statistics education

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    algebra, computer programming, and a year of calculus-based probability and statistics. Students wanting to obtain a doctorate in statistics from "any of the

  5. Sampling (statistics)

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    case classifier error over all the possible population statistics for class prior probabilities, would be the best. Accidental sampling (sometimes known

  6. Randomness

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    theories. The fields of mathematics, probability, and statistics use formal definitions of randomness. In statistics, a random variable is an assignment

  7. Beta distribution

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    In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] parametrized

  8. Markov chain Monte Carlo

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    In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov

  9. Calibration (statistics)

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    forecast skill. Calibration Calibrated probability assessment Upton, G, Cook, I. (2006) Oxford Dictionary of Statistics, OUP. ISBN 978-0-19-954145-4 Dawid

  10. Bayesian network

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    and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms can perform

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