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Bayesian Inference

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2001–2050 of 2226 papers

TitleStatusHype
Efficient Bayesian species tree inference under the multi-species coalescent—0
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation—0
Neuron's Eye View: Inferring Features of Complex Stimuli from Neural ResponsesCode0
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional DataCode0
Bayesian Matrix Completion via Adaptive Relaxed Spectral RegularizationCode0
Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition—0
The Population Posterior and Bayesian Modeling on Streams—0
Bidirectional Recurrent Neural Networks as Generative Models—0
Max-Margin Majority Voting for Learning from Crowds—0
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation—0
Bayesian inference via rejection filtering—0
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—0
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables—0
Bayesian Inference in Cumulative Distribution Fields—0
Toward Biochemical Probabilistic Computation—0
Biologically Inspired Dynamic Textures for Probing Motion Perception—0
Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems—0
Bayesian Inference of Online Social Network Statistics via Lightweight Random Walk Crawls—0
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
Modeling sequences and temporal networks with dynamic community structures—0
Bayesian inference for spatio-temporal spike-and-slab priors—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
The Population Posterior and Bayesian Inference on StreamsCode1
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
Sampling constrained probability distributions using Spherical Augmentation—0
Approximate Inference with the Variational Holder Bound—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
Automatic Variational Inference in Stan—0
Parallelizing MCMC with Random Partition TreesCode0
Provable Bayesian Inference via Particle Mirror Descent—0
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential FamiliesCode0
Variational Dropout and the Local Reparameterization TrickCode1
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep LearningCode1
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Benchmark Results

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