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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 2026–2050 of 2226 papers

TitleStatusHype
Distilling Model KnowledgeCode0
Bayesian Inference via Approximation of Log-likelihood for Priors in Exponential Family—0
Tractable Fully Bayesian Inference via Convex Optimization and Optimal Transport Theory—0
Unbiased Bayesian Inference for Population Markov Jump Processes via Random TruncationsCode0
A Simulated Annealing Approach to Bayesian Inference—0
Bayesian inference for spatio-temporal spike-and-slab priors—0
Modeling sequences and temporal networks with dynamic community structures—0
Learning without Recall by Random Walks on Directed Graphs—0
Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias—0
Varying-coefficient models with isotropic Gaussian process priors—0
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors—0
Towards Machine Wald—0
Orthogonal parallel MCMC methods for sampling and optimization—0
String and Membrane Gaussian Processes—0
Scalable Bayesian Inference for Excitatory Point Process NetworksCode0
Remarks on kernel Bayes' rule—0
Unifying Bayesian Inference and Vector Space Models for Improved Decipherment—0
Fast and accurate approximate inference of transcript expression from RNA-seq dataCode0
Scalable Discrete Sampling as a Multi-Armed Bandit Problem—0
Factorized Asymptotic Bayesian Inference for Factorial Hidden Markov Models—0
Approximate Inference with the Variational Holder Bound—0
Sampling constrained probability distributions using Spherical Augmentation—0
Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma AugmentationCode0
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random BasesCode0
Learning Deep Generative Models with Doubly Stochastic MCMC—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified