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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 151–200 of 2226 papers

TitleStatusHype
Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief PropagationCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Differentially Private Variational Inference for Non-conjugate ModelsCode1
Diffusion Models With Learned Adaptive NoiseCode1
BayesDLL: Bayesian Deep Learning LibraryCode1
Distilled Self-Critique of LLMs with Synthetic Data: a Bayesian PerspectiveCode1
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithmCode1
Domain Adaptation as a Problem of Inference on Graphical ModelsCode1
A Bit More Bayesian: Domain-Invariant Learning with UncertaintyCode1
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric ApproachesCode1
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantificationCode1
Enriching ImageNet with Human Similarity Judgments and Psychological EmbeddingsCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
Amortized Monte Carlo IntegrationCode1
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJα ModelCode1
Amortizing intractable inference in large language modelsCode1
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient DescentCode1
Flexible Bayesian Nonlinear Model ConfigurationCode1
A Simple Baseline for Bayesian Uncertainty in Deep LearningCode1
A practical tutorial on Variational BayesCode1
Fully Adaptive Bayesian Algorithm for Data Analysis, FABADACode1
GAN-based Priors for Quantifying UncertaintyCode1
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy dataCode1
Gaussian process learning of nonlinear dynamicsCode1
Laplacian Autoencoders for Learning Stochastic RepresentationsCode1
Learning by example: fast reliability-aware seismic imaging with normalizing flowsCode1
Learning to Generalize across Domains on Single Test SamplesCode1
Lifelong Incremental Reinforcement Learning with Online Bayesian InferenceCode1
Low-rank extended Kalman filtering for online learning of neural networks from streaming dataCode1
Memory-Based Meta-Learning on Non-Stationary DistributionsCode1
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
Particle Flow Bayes' RuleCode1
Accelerated Bayesian SED Modeling using Amortized Neural Posterior EstimationCode1
Monte Carlo guided Diffusion for Bayesian linear inverse problemsCode1
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time SeriesCode1
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive ModelsCode1
Understanding and Accelerating Particle-Based Variational InferenceCode1
Calibrating Transformers via Sparse Gaussian ProcessesCode1
Parallelized Acquisition for Active Learning using Monte Carlo SamplingCode1
Parallel Streaming Wasserstein BarycentersCode1
Efficient Online Bayesian Inference for Neural BanditsCode1
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inferenceCode1
A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection areaCode1
Validated Variational Inference via Practical Posterior Error BoundsCode1
Probabilistic AutoencoderCode1
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential EquationsCode1
Projected Stein Variational Gradient DescentCode1
Pick-and-Mix Information Operators for Probabilistic ODE SolversCode1
Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical ImagingCode1
πVAE: a stochastic process prior for Bayesian deep learning with MCMCCode1
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Benchmark Results

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