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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 1751–1800 of 2226 papers

TitleStatusHype
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process—0
Accelerated physical emulation of Bayesian inference in spiking neural networks—0
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)Code1
Understanding and Accelerating Particle-Based Variational InferenceCode1
Scalable approximate Bayesian inference for particle tracking dataCode0
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)—0
Rating Distributions and Bayesian Inference: Enhancing Cognitive Models of Spatial Language Use—0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology—0
Random Feature Stein DiscrepanciesCode0
Large-Scale Stochastic Sampling from the Probability SimplexCode0
Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning—0
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational InferenceCode0
Reconstructing networks with unknown and heterogeneous errors—0
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-DivergencesCode0
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference—0
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing—0
Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filterCode0
Efficient Bayesian Inference for a Gaussian Process Density Model—0
Wasserstein Variational Inference—0
Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement LearningCode0
Semi-Implicit Variational InferenceCode0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
Likelihood-free inference with emulator networksCode0
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers—0
Bayesian Inference of Regular Expressions from Human-Generated Example Strings—0
Bayesian posterior approximation via greedy particle optimization—0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
Nonparametric Bayesian Deep Networks with Local CompetitionCode0
Approximate Bayesian inference in spatial environments—0
Bayesian Joint Spike-and-Slab Graphical LassoCode0
Wasserstein Measure Coresets—0
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive FlowsCode1
Spatial Uncertainty Sampling for End-to-End Control—0
Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlo—0
Subsampling Sequential Monte Carlo for Static Bayesian Models—0
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in BadlandsCode0
A Channel-based Exact Inference Algorithm for Bayesian Networks—0
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems—0
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning—0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
Mean Field Network based Graph Refinement with application to Airway Tree Extraction—0
A Compressed Sensing Approach for Distribution Matching—0
Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference—0
Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models—0
Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information—0
Locally Private Bayesian Inference for Count ModelsCode0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels—0
A Probabilistic Disease Progression Model for Predicting Future Clinical OutcomeCode0
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Benchmark Results

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