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  2. Bayesian statistics - Wikipedia

    en.wikipedia.org/wiki/Bayesian_statistics

    Bayesian statistics (/ ˈ b eɪ z i ə n / BAY-zee-ən or / ˈ b eɪ ʒ ən / BAY-zhən) [1] is a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous ...

  3. Bayesian cognitive science - Wikipedia

    en.wikipedia.org/wiki/Bayesian_cognitive_science

    Bayesian cognitive science, also known as computational cognitive science, is an approach to cognitive science concerned with the rational analysis [1] of cognition through the use of Bayesian inference and cognitive modeling. The term "computational" refers to the computational level of analysis as put forth by David Marr. [2]

  4. Bayesian inference - Wikipedia

    en.wikipedia.org/wiki/Bayesian_inference

    Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data . Bayesian inference has found application in a wide range of activities, including science , engineering , philosophy , medicine , sport , and law .

  5. File:Stochastic Normalisations as Bayesian Learning.pdf

    en.wikipedia.org/wiki/File:Stochastic...

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  6. Bayesian probability - Wikipedia

    en.wikipedia.org/wiki/Bayesian_probability

    Bayesian probability (/ ˈ b eɪ z i ə n / BAY-zee-ən or / ˈ b eɪ ʒ ən / BAY-zhən) [1] is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation [2] representing a state of knowledge [3] or as quantification of a personal belief.

  7. Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Bayes_classifier

    In practice, as in most of statistics, the difficulties and subtleties are associated with modeling the probability distributions effectively—in this case, ⁡ (= =). The Bayes classifier is a useful benchmark in statistical classification.

  8. Bayesian experimental design - Wikipedia

    en.wikipedia.org/wiki/Bayesian_experimental_design

    The most common approach is to use Markov chain Monte Carlo methods to generate samples from the posterior, which can then be used to approximate the expected utility. Another approach is to use a variational Bayes approximation of the posterior, which can often be calculated in closed form. This approach has the advantage of being ...

  9. Bayesian approaches to brain function - Wikipedia

    en.wikipedia.org/wiki/Bayesian_approaches_to...

    This field of study has its historical roots in numerous disciplines including machine learning, experimental psychology and Bayesian statistics.As early as the 1860s, with the work of Hermann Helmholtz in experimental psychology, the brain's ability to extract perceptual information from sensory data was modeled in terms of probabilistic estimation.