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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 1451–1500 of 2226 papers

TitleStatusHype
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon SynapseCode0
Mutually Regressive Point ProcessesCode0
AGEM: Solving Linear Inverse Problems via Deep Priors and SamplingCode0
Efficient Approximate Inference with Walsh-Hadamard Variational Inference—0
Transflow Learning: Repurposing Flow Models Without Retraining—0
A Multilayered Block Network Model to Forecast Large Dynamic Transportation Graphs: an Application to US Air Transport—0
Learning of Weighted Multi-layer Networks via Dynamic Social Spaces, with Application to Financial Interbank TransactionsCode0
Differentially Private Federated Variational InferenceCode0
Measuring Uncertainty through Bayesian Learning of Deep Neural Network StructureCode0
Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference—0
Improved algorithm for neuronal ensemble inference by Monte Carlo method—0
A Bayesian/Information Theoretic Model of Bias Learning—0
Streaming Bayesian Inference for Crowdsourced Classification—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess HypothesesCode0
Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set—0
Variational Bayesian inference of hidden stochastic processes with unknown parameters—0
Phase transitions and optimal algorithms for semi-supervised classifications on graphs: from belief propagation to graph convolution network—0
Continual Multi-task Gaussian ProcessesCode0
Bayesian causal inference via probabilistic program synthesis—0
Parameter elimination in particle Gibbs samplingCode0
A Hamilton-Jacobi Reachability-Based Framework for Predicting and Analyzing Human Motion for Safe Planning—0
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation—0
Stein Variational Gradient Descent With Matrix-Valued KernelsCode0
Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models—0
We Know Where We Don't Know: 3D Bayesian CNNs for Credible Geometric UncertaintyCode0
Variational Predictive Information Bottleneck—0
Safe-Bayesian Generalized Linear Regression—0
Why bigger is not always better: on finite and infinite neural networks—0
Efficient Bayesian Inference for Nested Simulators—0
Graph Tracking in Dynamic Probabilistic Programs via Source Transformations—0
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes—0
Bijectors.jl: Flexible transformations for probability distributionsCode0
Neural Permutation Processes—0
Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors—0
Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs—0
Stein Variational Gradient Descent for Approximate Bayesian Computation—0
Variationally Inferred Sampling Through a Refined BoundCode0
Interpretable User Models via Decision-rule Gaussian Processes: Preliminary Results on Energy Storage—0
Exchangeable Variational Autoencoders with Applications to Genomic Data—0
AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithmsCode0
Challenges in Markov chain Monte Carlo for Bayesian neural networksCode0
Bayesian Integration of Multi-resolutional Grid Codes for Spatial Cognition—0
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte CarloCode1
Variational Tracking and Prediction with Generative Disentangled State-Space Models—0
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Validated Variational Inference via Practical Posterior Error BoundsCode1
Distilling Importance Sampling for Likelihood Free InferenceCode0
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Benchmark Results

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