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  2. Template:Bayesian statistics - Wikipedia

    en.wikipedia.org/wiki/Template:Bayesian_statistics

    Variational inference; Approximate Bayesian computation; Estimators; Bayesian estimator; Credible interval; Maximum a posteriori estimation; Evidence approximation; Evidence lower bound; Nested sampling; Model evaluation; Bayes factor (Schwarz criterion) Model averaging; Posterior predictive; Mathematics portal

  3. 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.

  4. 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 ...

  5. 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

  6. 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 ().

  7. 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

  8. Statistical inference - Wikipedia

    en.wikipedia.org/wiki/Statistical_inference

    Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling.Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model.

  9. 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.