• GLLAMM

  • Referenced in 61 articles [sw06517]
  • gllamm after installation). gllamm maximises the marginal log-likelihood using Stata’s version ... discrete random effects or factors, the marginal log-likelihood is evaluated exactly whereas numerical integration...
  • MIXOR

  • Referenced in 26 articles [sw08990]
  • ordinal regression analysis. MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic ... population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution...
  • ltm

  • Referenced in 39 articles [sw07911]
  • available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule...
  • George

  • Referenced in 20 articles [sw29786]
  • focused on efficiently evaluating the marginalized likelihood of a dataset under a GP prior, even...
  • ABC-SubSim

  • Referenced in 13 articles [sw10099]
  • provides an estimate of the evidence (marginal likelihood) for posterior model class assessment...
  • MIXNO

  • Referenced in 9 articles [sw10546]
  • nominal logistic regression. MIXNO provides maximum marginal likelihood estimates for mixed-effects nominal logistic regression ... nesting of the data. MIXNO uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution...
  • EbayesThresh

  • Referenced in 17 articles [sw11103]
  • parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive...
  • copula

  • Referenced in 136 articles [sw07944]
  • display. Fitting copula-based models with maximum likelihood method is provided as template examples. With ... easily extended by user-defined copulas and margins to solve problems...
  • bridgesampling

  • Referenced in 8 articles [sw21804]
  • computation of normalizing constants (e.g., marginal likelihoods). These normalizing constants are notoriously difficult to obtain...
  • dynesty

  • Referenced in 8 articles [sw28387]
  • estimate Bayesian posteriors and evidences (marginal likelihoods) using Dynamic Nested Sampling. By adaptively allocating samples...
  • gprege

  • Referenced in 8 articles [sw31093]
  • observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts...
  • Medlda

  • Referenced in 12 articles [sw11723]
  • Medlda: maximum margin supervised topic models. A supervised topic model can use side information such ... However, existing supervised topic models predominantly employ likelihood-driven objective functions for learning and inference ... leaving the popular and potentially powerful max-margin principle unexploited for seeking predictive representations ... applied for jointly max-margin and maximum likelihood learning of directed or undirected topic models...
  • HGLMMM

  • Referenced in 6 articles [sw08092]
  • first order Laplace approximation to the marginal likelihood. Dispersion parameters are estimated using first order...
  • bfa

  • Referenced in 25 articles [sw07430]
  • semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation...
  • horseshoe

  • Referenced in 6 articles [sw29790]
  • prior or estimated via maximum marginal likelihood estimation (MMLE). The main function, horseshoe...
  • GSPPCA

  • Referenced in 4 articles [sw25977]
  • first exact computation of the marginal likelihood of a Bayesian PCA model. Moreover, in order ... variational expectation-maximization algorithm. The exact marginal likelihood can eventually be maximized over this path...
  • MIXREG

  • Referenced in 5 articles [sw24547]
  • adjusting for clustering. MIXREG uses maximum marginal likelihood estimation, utilizing both the EM algorithm...
  • parfm

  • Referenced in 11 articles [sw19053]
  • estimation is done by maximising the marginal log-likelihood, with right-censored and possibly left...
  • DNest4

  • Referenced in 3 articles [sw25767]
  • assumed prior information. The marginal likelihood, also known as the ”evidence”, is a key quantity ... generating posterior samples and estimating marginal likelihoods. It is effective at solving complex problems including...