The following pages link to (Q2933843):
Displaying 50 items.
- Variational Inference for Stochastic Block Models From Sampled Data (Q99254) (← links)
- Variational inference for generalized linear mixed models using partially noncentered parametrizations (Q252744) (← links)
- Topic-adjusted visibility metric for scientific articles (Q288549) (← links)
- Nonparametric Bayesian topic modelling with the hierarchical Pitman-Yor processes (Q324682) (← links)
- Variational inference for sparse spectrum Gaussian process regression (Q341145) (← links)
- Stochastic variational inference for large-scale discrete choice models using adaptive batch sizes (Q517404) (← links)
- Sparse polynomial chaos expansions using variational relevance vector machines (Q781971) (← links)
- Scalable Bayesian preference learning for crowds (Q782436) (← links)
- Fast Bayesian estimation of spatial count data models (Q830478) (← links)
- A stochastic variational framework for fitting and diagnosing generalized linear mixed models (Q899068) (← links)
- Fixed-form variational posterior approximation through stochastic linear regression (Q908038) (← links)
- An online expectation maximization algorithm for exploring general structure in massive networks (Q1618786) (← links)
- Variational Hamiltonian Monte Carlo via score matching (Q1631559) (← links)
- A general method for robust Bayesian modeling (Q1631602) (← links)
- Online multi-label dependency topic models for text classification (Q1640575) (← links)
- Scalable methods for Bayesian selective inference (Q1657959) (← links)
- A Bayesian approach to multiscale inverse problems with on-the-fly scale determination (Q1674657) (← links)
- Scalable information inequalities for uncertainty quantification (Q1685620) (← links)
- Slow mixing for latent Dirichlet allocation (Q1687198) (← links)
- Sparse probit linear mixed model (Q1698863) (← links)
- Gaussian variational approximation with sparse precision matrices (Q1702005) (← links)
- Variational Bayes with synthetic likelihood (Q1704030) (← links)
- Comment: Consensus Monte Carlo using expectation propagation (Q1705541) (← links)
- Stochastic variational hierarchical mixture of sparse Gaussian processes for regression (Q1722733) (← links)
- PRISM revisited: declarative implementation of a probabilistic programming language using multi-prompt delimited control (Q1726305) (← links)
- Fast approximation of variational Bayes Dirichlet process mixture using the maximization-maximization algorithm (Q1726386) (← links)
- Multi-rubric models for ordinal spatial data with application to online ratings data (Q1728631) (← links)
- Variational inference for probabilistic Poisson PCA (Q1728678) (← links)
- Variational message passing for elaborate response regression models (Q1738138) (← links)
- Robust unsupervised cluster matching for network data (Q1741172) (← links)
- Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks (Q1783942) (← links)
- Consistency of variational Bayes inference for estimation and model selection in mixtures (Q1786584) (← links)
- Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation (Q1790387) (← links)
- Trial-and-error or avoiding a guess? Initialization of the Kalman filter (Q2003835) (← links)
- Hierarchical Dirichlet scaling process (Q2014581) (← links)
- Large scale multi-label learning using Gaussian processes (Q2051297) (← links)
- Topic extraction from extremely short texts with variational manifold regularization (Q2051303) (← links)
- Infinite-dimensional gradient-based descent for alpha-divergence minimisation (Q2054493) (← links)
- Locally induced Gaussian processes for large-scale simulation experiments (Q2058747) (← links)
- Variational Bayes on manifolds (Q2058893) (← links)
- Updating variational Bayes: fast sequential posterior inference (Q2066743) (← links)
- Product-form estimators: exploiting independence to scale up Monte Carlo (Q2066757) (← links)
- Variational inference and sparsity in high-dimensional deep Gaussian mixture models (Q2080343) (← links)
- Mapping interstellar dust with Gaussian processes (Q2080783) (← links)
- Large scale tensor regression using kernels and variational inference (Q2102330) (← links)
- Adaptive infinite dropout for noisy and sparse data streams (Q2102355) (← links)
- \(\pi\) VAE: a stochastic process prior for Bayesian deep learning with MCMC (Q2103969) (← links)
- Bayesian learning via neural Schrödinger-Föllmer flows (Q2104005) (← links)
- Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration (Q2110192) (← links)
- A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
