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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. ArviZ - Wikipedia

    en.wikipedia.org/wiki/ArviZ

    Bambi is a high-level Bayesian model-building interface based on PyMC; PyMC a probabilistic programming language written in Python; Stan is a probabilistic programming language for statistical inference written in C++

  4. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    This statistics -related article is a stub. You can help Wikipedia by expanding it.

  5. PyMC - Wikipedia

    en.wikipedia.org/wiki/PyMC

    PyMC (formerly known as PyMC3) is a probabilistic programming language written in Python. It can be used for Bayesian statistical modeling and probabilistic machine learning. PyMC performs inference based on advanced Markov chain Monte Carlo and/or variational fitting algorithms.

  6. Recursive Bayesian estimation - Wikipedia

    en.wikipedia.org/wiki/Recursive_Bayesian_estimation

    In probability theory, statistics, and machine learning, recursive Bayesian estimation, also known as a Bayes filter, is a general probabilistic approach for estimating an unknown probability density function recursively over time using incoming measurements and a mathematical process model.

  7. 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.

  8. Nested sampling algorithm - Wikipedia

    en.wikipedia.org/wiki/Nested_sampling_algorithm

    It is an alternative to methods from the Bayesian literature [3] such as bridge sampling and defensive importance sampling. Here is a simple version of the nested sampling algorithm, followed by a description of how it computes the marginal probability density Z = P ( D ∣ M ) {\displaystyle Z=P(D\mid M)} where M {\displaystyle M} is M 1 ...

  9. Bayesian network - Wikipedia

    en.wikipedia.org/wiki/Bayesian_network

    Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.