• RStan

  • Referenced in 57 articles [sw13990]
  • Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation...
  • rstan

  • Referenced in 27 articles [sw16103]
  • Monte Carlo, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation...
  • UFL

  • Referenced in 56 articles [sw11183]
  • view to numerical approximation. Features of UFL include support for variational forms and functionals, automatic...
  • ADVI

  • Referenced in 24 articles [sw34040]
  • scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations...
  • JIVE

  • Referenced in 12 articles [sw09511]
  • Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such data ... three terms: a low-rank approximation capturing joint variation across data types, low-rank approximations...
  • COBRA

  • Referenced in 26 articles [sw02211]
  • COBRA, this approximate eigenvalue is further refined using a variational principle to obtain fourth-order...
  • BayesLCA

  • Referenced in 8 articles [sw16118]
  • maximization algorithm, Gibbs sampling and a variational Bayes approximation. The article briefly outlines the methodology...
  • Breach

  • Referenced in 20 articles [sw20822]
  • respect to parameters variation. The latter is used to perform approximate reachability analysis and parameter...
  • GPflow

  • Referenced in 12 articles [sw21518]
  • that it uses variational inference as the primary approximation method, provides concise code through...
  • PMTBR

  • Referenced in 17 articles [sw02086]
  • intermediate between frequency domain projection methods and approximation of truncated balanced realizations. The methods discussed ... presence of parameter change due to process variation...
  • VFGEN

  • Referenced in 14 articles [sw00995]
  • field with its variational equation, converting delay equations to finite dimensional approximations, and for generating...
  • CMA-ES

  • Referenced in 106 articles [sw05063]
  • solutions, denoted as x) are generated by variation, usually in a stochastic way, and then ... underlying objective function similar to the approximation of the inverse Hessian matrix in the Quasi...
  • zoverw

  • Referenced in 8 articles [sw25940]
  • ratio z/w for any two jointly normal variates z,w, and provides details on methods ... practical considerations suggest there should be approximations whose adequacy can be verified by means ... software. These approximations show that many of the ratios of normal variates encountered in practice ... ratio (a+x)/(b+y) is itself approximately normally distributed with mean...
  • varbvs

  • Referenced in 1 article [sw21809]
  • algorithms are based on the variational approximations described in ”Scalable variational inference for Bayesian variable...
  • MAP

  • Referenced in 6 articles [sw11471]
  • conservative equations. The total variation diminishing (TVD) limiters and approximate Riemann solvers are also equipped...
  • ProSper

  • Referenced in 1 article [sw30605]
  • data, and they provide rich a-posteriori approximations for inference. The library is designed ... scalable due to a combination of variational approximations and parallelization. Implementations of all algorithms allow...
  • DSPCA

  • Referenced in 35 articles [sw04804]
  • examine the problem of approximating, in the Frobenius-norm sense, a positive, semidefinite symmetric matrix ... modification of the classical variational representation of the largest eigenvalue of a symmetric matrix, where...
  • VBLPCM

  • Referenced in 1 article [sw18753]
  • network data, using a fast Variational Bayes approximation...
  • vbmp

  • Referenced in 2 articles [sw35354]
  • class membership posterior probability employing variational and sparse approximation to the full posterior. This software...