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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 726–750 of 2226 papers

TitleStatusHype
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning—0
A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias—0
Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication—0
Push: Concurrent Probabilistic Programming for Bayesian Deep LearningCode0
Bayesian Knowledge-driven Critiquing with Indirect Evidence—0
In-Context Learning through the Bayesian PrismCode0
Entropy-based Training Methods for Scalable Neural Implicit Sampler—0
Latent Optimal Paths by Gumbel Propagation for Variational Bayesian Dynamic ProgrammingCode0
Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging—0
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive AdvantagesCode0
Robust Bayesian Inference for Berkson and Classical Measurement Error Models—0
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in KenyaCode0
On Mixing Rates for Bayesian CART—0
Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study—0
Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Models—0
Sparse species interactions reproduce abundance correlation patterns in microbial communities—0
A Bayesian sparse factor model with adaptive posterior concentration—0
Bayesian inference and neural estimation of acoustic wave propagation—0
Improving Neural Additive Models with Bayesian PrinciplesCode0
Pedestrian Trajectory Forecasting Using Deep Ensembles Under Sensing Uncertainty—0
Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks—0
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo AlgorithmsCode0
Adversarial robustness of amortized Bayesian inferenceCode0
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior InferenceCode0
Bayesian calibration of differentiable agent-based models—0
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Benchmark Results

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