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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 151–175 of 2226 papers

TitleStatusHype
D3p -- A Python Package for Differentially-Private Probabilistic ProgrammingCode1
Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty QuantificationCode1
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric ApproachesCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Diffusion Models With Learned Adaptive NoiseCode1
Diffusive Gibbs SamplingCode1
A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection areaCode1
Distilled Self-Critique of LLMs with Synthetic Data: a Bayesian PerspectiveCode1
A practical tutorial on Variational BayesCode1
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetsCode1
Efficient Online Bayesian Inference for Neural BanditsCode1
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaicsCode1
Eryn : A multi-purpose sampler for Bayesian inferenceCode1
Amortized Monte Carlo IntegrationCode1
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming ApproachCode1
Amortizing intractable inference in large language modelsCode1
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel RecombinationCode1
FedBE: Making Bayesian Model Ensemble Applicable to Federated LearningCode1
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
ForecastPFN: Synthetically-Trained Zero-Shot ForecastingCode1
Bayesian Coresets: Revisiting the Nonconvex Optimization PerspectiveCode1
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
GAN-based Priors for Quantifying UncertaintyCode1
GATSBI: Generative Adversarial Training for Simulation-Based InferenceCode1
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context LearningCode1
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Benchmark Results

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