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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. Empirical Bayes method - Wikipedia

    en.wikipedia.org/wiki/Empirical_Bayes_method

    Empirical Bayes methods can be seen as an approximation to a fully Bayesian treatment of a hierarchical Bayes model.. In, for example, a two-stage hierarchical Bayes model, observed data = {,, …,} are assumed to be generated from an unobserved set of parameters = {,, …,} according to a probability distribution ().

  4. Variational message passing - Wikipedia

    en.wikipedia.org/wiki/Variational_message_passing

    The likelihood estimate needs to be as large as possible; because it's a lower bound, getting closer ⁡ improves the approximation of the log likelihood. By substituting in the factorized version of , (), parameterized over the hidden nodes as above, is simply the negative relative entropy between and plus other terms independent of if is defined as

  5. 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.

  6. 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 ...

  7. Laplace's approximation - Wikipedia

    en.wikipedia.org/wiki/Laplace's_approximation

    Model building; Conjugate prior; Linear regression; Empirical Bayes; Hierarchical model; Posterior approximation; Markov chain Monte Carlo; Laplace's approximation; Integrated nested Laplace approximations; Variational inference; Approximate Bayesian computation; Estimators; Bayesian estimator; Credible interval; Maximum a posteriori estimation ...

  8. Pyro Plastics Corporation - Wikipedia

    en.wikipedia.org/wiki/Pyro_Plastics_Corporation

    The Pyro Plastics Corporation was an American manufacturing company based in Union Township, NJ and popular during the 1950s and 1960s that produced toys and plastic model kits. Some of the scale models manufactured and commercialised by Pyro were cars, motorcycles, aircraft, ships, and military vehicles, and animal and human figures.

  9. Bernstein–von Mises theorem - Wikipedia

    en.wikipedia.org/wiki/Bernstein–von_Mises_theorem

    In Bayesian inference, the Bernstein–von Mises theorem provides the basis for using Bayesian credible sets for confidence statements in parametric models.It states that under some conditions, a posterior distribution converges in total variation distance to a multivariate normal distribution centered at the maximum likelihood estimator ^ with covariance matrix given by (), where is the true ...