enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. 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.

  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. 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.

  5. 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

  6. 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.

  7. Bayesian inference in phylogeny - Wikipedia

    en.wikipedia.org/wiki/Bayesian_inference_in...

    Bayesian inference of phylogeny combines the information in the prior and in the data likelihood to create the so-called posterior probability of trees, which is the probability that the tree is correct given the data, the prior and the likelihood model.

  8. 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 ().

  9. 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.