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  2. PyMC - Wikipedia

    en.wikipedia.org/wiki/PyMC

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

  3. Variational Bayesian methods - Wikipedia

    en.wikipedia.org/wiki/Variational_Bayesian_methods

    Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning.They are typically used in complex statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with various sorts of relationships among the three types of random variables, as ...

  4. Reparameterization trick - Wikipedia

    en.wikipedia.org/wiki/Reparameterization_trick

    The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational inference, variational autoencoders, and stochastic optimization.

  5. Approximate Bayesian computation - Wikipedia

    en.wikipedia.org/wiki/Approximate_Bayesian...

    Engine for Likelihood-Free Inference. ELFI is a statistical software package written in Python for Approximate Bayesian Computation (ABC), also known e.g. as likelihood-free inference, simulator-based inference, approximative Bayesian inference etc. [83] ABCpy: Python package for ABC and other likelihood-free inference schemes.

  6. Bayesian experimental design - Wikipedia

    en.wikipedia.org/wiki/Bayesian_experimental_design

    In numerous publications on Bayesian experimental design, it is (often implicitly) assumed that all posterior probabilities will be approximately normal. This allows for the expected utility to be calculated using linear theory, averaging over the space of model parameters. [2]

  7. Bayesian statistics - Wikipedia

    en.wikipedia.org/wiki/Bayesian_statistics

    Exploratory analysis of Bayesian models is an adaptation or extension of the exploratory data analysis approach to the needs and peculiarities of Bayesian modeling. In the words of Persi Diaconis: [16] Exploratory data analysis seeks to reveal structure, or simple descriptions in data. We look at numbers or graphs and try to find patterns.

  8. Hyperparameter (Bayesian statistics) - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_(Bayesian...

    In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for the underlying system under analysis. For example, if one is using a beta distribution to model the distribution of the parameter p of a Bernoulli distribution, then:

  9. Laplace's approximation - Wikipedia

    en.wikipedia.org/wiki/Laplace's_approximation

    Bayesian inference; Bayesian probability; Bayes' theorem; Bernstein–von Mises theorem; Coherence; Cox's theorem; Cromwell's rule; Likelihood principle; Principle of indifference; Principle of maximum entropy; Model building; Conjugate prior; Linear regression; Empirical Bayes; Hierarchical model; Posterior approximation; Markov chain Monte ...