• PROC NLMIXED

  • Referenced in 70 articles [sw11039]
  • mixed models by maximizing an approximation to the likelihood integrated over the random effects. Different ... their approximate standard errors based on the second derivative matrix of the likelihood function. PROC ... nonrandom parameters, and PROC NLMIXED computes their approximate standard errors by using the delta method...
  • RStan

  • Referenced in 87 articles [sw13990]
  • Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization...
  • rstan

  • Referenced in 55 articles [sw16103]
  • Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization...
  • MsdeParEst

  • Referenced in 22 articles [sw25419]
  • diffusion or both. Approximate maximum likelihood methods are used. M. Delattre, V. Genon-Catalot...
  • PhyML

  • Referenced in 11 articles [sw10915]
  • PhyML is a software that estimates maximum likelihood phylogenies from alignments of nucleotide or amino ... parametric bootstrap and the approximate likelihood ratio test). (http://packages.ubuntu.com/lucid/science/phyml)...
  • glmm

  • Referenced in 3 articles [sw36739]
  • Linear Mixed Models via Monte Carlo Likelihood Approximation. Approximates the likelihood of a generalized linear ... mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum...
  • ordinal

  • Referenced in 31 articles [sw12561]
  • maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite ... methods and slice methods for visualizing the likelihood function and checking convergence...
  • bernor

  • Referenced in 10 articles [sw19665]
  • Monte Carlo method to approximate the maximum likelihood estimate (MLE), when there are missing data ... observed data likelihood is not available in closed form. This method uses simulated missing data ... observed data. Our Monte Carlo approximation to the MLE is a consistent and asymptotically normal...
  • FastTree

  • Referenced in 9 articles [sw28979]
  • Distance Matrix. FastTree infers approximately-maximum-likelihood phylogenetic trees from alignments of nucleotide or protein ... single rate for each site (the ”CAT” approximation). To quickly estimate the reliability of each...
  • glmmML

  • Referenced in 9 articles [sw07509]
  • Gauss-Hermite and Laplace quadrature approximations of the likelihood function. The fixed effects model...
  • QUIC

  • Referenced in 34 articles [sw11795]
  • quadratic approximation for sparse inverse covariance estimation. The ℓ 1 -regularized Gaussian maximum likelihood estimator ... Newton’s method and employs a quadratic approximation, but with some modifications that leverage...
  • DR-ABC

  • Referenced in 3 articles [sw24742]
  • Approximate Bayesian Computation with Kernel-Based Distribution Regression. Performing exact posterior inference in complex generative ... expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework ... that constructs an approximation to the true likelihood based on the similarity between the observed ... crucially influences the quality of the likelihood approximation and hence also the quality...
  • epiABC

  • Referenced in 10 articles [sw23334]
  • stochastic epidemic models using approximate Bayesian computation. Likelihood-based inference for disease outbreak data ... incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for the analysis ... epidemic models without having to calculate the likelihood of the observed data. We consider both...
  • GAKREM

  • Referenced in 10 articles [sw02712]
  • used first. Second, the log-likelihood of each configuration of parameters and the number ... each evolving generation it efficiently approximates the log-likelihood for each chromosome using logarithmic regression...
  • frailtyHL

  • Referenced in 10 articles [sw19049]
  • gamma are allowed. The h-likelihood uses the Laplace approximation when the numerical integration...
  • sdetorus

  • Referenced in 2 articles [sw20248]
  • analogues of the Ornstein-Uhlenbeck process. Their likelihood function is a product of transition densities ... through adequate schemes. We propose three approximate likelihoods that are computationally tractable: (i) a likelihood ... pseudo-likelihoods; (iii) a likelihood based on a specific approximation to the transition density ... investigates the empirical performance of the approximate likelihoods. Finally, two diffusions are used to model...
  • AS 191

  • Referenced in 3 articles [sw03889]
  • Algorithm AS 191. An algorithm for approximate likelihood calculation of ARMA and seasonal ARMA models...
  • GLMMadaptive

  • Referenced in 3 articles [sw34056]
  • single grouping factor under maximum likelihood approximating the integrals over the random effects with...
  • FuturCMB

  • Referenced in 3 articles [sw41039]
  • parameter errors when the individual parameter likelihoods approximate a Gaussian distribution. However, where Gaussianity...
  • HGLMMM

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