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  2. Template:Bayesian statistics - Wikipedia

    en.wikipedia.org/wiki/Template:Bayesian_statistics

    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 ...

  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. 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.

  6. 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

  7. Template:Convex analysis and variational analysis - Wikipedia

    en.wikipedia.org/wiki/Template:Convex_analysis...

    To change this template's initial visibility, the |state= parameter may be used: {{Convex analysis and variational analysis | state = collapsed}} will show the template collapsed, i.e. hidden apart from its title bar. {{Convex analysis and variational analysis | state = expanded}} will show the template expanded, i.e. fully visible.

  8. File:Ladder of inference.svg - Wikipedia

    en.wikipedia.org/wiki/File:Ladder_of_inference.svg

    English: The ladder of inference is metaphorical model, created by Chris Argyris (1923–2013), of how people take action based on an often unconscious process of inference from the flux or pool of observable "data". Argyris's original ladder had fewer rungs with different names.

  9. Approximate Bayesian computation - Wikipedia

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

    In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for ...