• hdi

  • Referenced in 26 articles [sw22818]
  • High-Dimensional Inference. Implementation of multiple approaches to perform inference in high-dimensional models...
  • EigenPrism

  • Referenced in 15 articles [sw21947]
  • EigenPrism: inference for high dimensional signal-to-noise ratios. Consider the following three important problems ... statistical inference: constructing confidence intervals for the error of a high dimensional (p>n) regression ... performing inference on the l 2 -norm of the signal in high dimensional linear regression...
  • ROCKET

  • Referenced in 13 articles [sw30016]
  • variables is of fundamental importance in high-dimensional statistics, with numerous applications in biological ... focus on inference for edge parameters in a high-dimensional transelliptical model, which generalizes Gaussian ... normality of ROCKET in an ultra high-dimensional setting under mild assumptions, without relying ... Gaussian models in terms of achieving accurate inference on simulated data. We also compare...
  • VBayesLab

  • Referenced in 5 articles [sw41752]
  • models. Efficient computational methods for high-dimensional Bayesian inference are developed using Gaussian variational approximation ... Bayesian inference approach lead to a regression and classification method that has a high prediction...
  • hdm

  • Referenced in 7 articles [sw21313]
  • selected high-dimensional statistical and econometric methods for estimation and inference. Efficient estimators and uniformly ... structural parameters are provided which appear in high-dimensional approximately sparse models. Including functions ... high-dimensional setting. Moreover, the methods enable valid post-selection inference and rely...
  • SIHR

  • Referenced in 2 articles [sw39919]
  • package SIHR: Statistical Inference in High Dimensional Regression. Inference procedures in the high-dimensional setting...
  • FFJORD

  • Referenced in 9 articles [sw34244]
  • approach on high-dimensional density estimation, image generation, and variational inference, achieving the state...
  • DREAM

  • Referenced in 9 articles [sw24746]
  • unknown parameter values, x are subject to inference using the data Y ¿ . Unfortunately, for complex ... models the posterior distribution is often high dimensional and analytically intractable, and sampling methods ... 2008a, 2009a) and used for Bayesian inference in fields ranging from physics, chemistry and engineering ... sampling problems involving (among others) bimodality, high-dimensionality, summary statistics, bounded parameter spaces, dynamic simulation...
  • GANomaly

  • Referenced in 5 articles [sw41240]
  • learns the generation of high-dimensional image space and the inference of latent space. Employing...
  • PersistenceImages

  • Referenced in 40 articles [sw41418]
  • convert a PD to a finite-dimensional vector representation which we call a persistence image ... features containing discriminating topological information. Finally, high accuracy inference of parameter values from the dynamic...
  • polychord

  • Referenced in 12 articles [sw25768]
  • PolyChord is a Bayesian inference tool for the simultaneous calculation of evidences and sampling ... MultiNest. It performs well on moderately high dimensional ( 100s D) posterior distributions, and can cope...
  • hierinf

  • Referenced in 4 articles [sw33471]
  • hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised...
  • PyDREAM

  • Referenced in 1 article [sw34672]
  • PyDREAM: high-dimensional parameter inference for biological models in python. : Biological models contain many parameters ... exhibit slow or premature convergence in high-dimensional search spaces. Here, we present PyDREAM ... advantage of distributed computing resources, facilitating parameter inference and uncertainty estimation of CPU-intensive biological...
  • GGMselect

  • Referenced in 9 articles [sw10347]
  • Graph selection with GGMselect. Applications on inference of biological networks have raised a strong interest ... problem of graph estimation in high-dimensional Gaussian graphical models. To handle this problem...
  • ADMB

  • Referenced in 17 articles [sw07416]
  • builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Many criteria ... framework based on automatic differentiation, aimed at highly nonlinear models with a large number ... using AD are computational efficiency and high numerical accuracy, both crucial in many practical problems ... generic implementation of Laplace approximation of high-dimensional integrals for use in latent variable models...
  • EEBoost

  • Referenced in 1 article [sw26353]
  • which can be applied in high-dimensional settings where inference for low-dimensional parameters would...
  • HDTD

  • Referenced in 2 articles [sw38988]
  • Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data ... called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference...
  • RCAL

  • Referenced in 2 articles [sw33473]
  • estimation for causal inference and missing-data problems with high-dimensional data, based...
  • TempoGAN

  • Referenced in 10 articles [sw42570]
  • designed for the inference of three-dimensional volumetric data, our model generates consistent and detailed ... generator is able to infer more realistic high-resolution details by using additional physical quantities...
  • mvPot

  • Referenced in 1 article [sw42605]
  • Extreme Events. Tools for high-dimensional peaks-over-threshold inference and simulation of spatial extremal...