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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 2051–2075 of 2226 papers

TitleStatusHype
Sampling with Trusthworthy Constraints: A Variational Gradient FrameworkCode0
A stochastic Stein Variational Newton methodCode0
Scalable approximate Bayesian inference for particle tracking dataCode0
Normalizing Constant Estimation with Gaussianized Bridge SamplingCode0
The Neural Moving Average Model for Scalable Variational Inference of State Space ModelsCode0
NFAD: Fixing anomaly detection using normalizing flowsCode0
Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess HypothesesCode0
Assumed Density Filtering Q-learningCode0
Scalable Bayesian Inference for Excitatory Point Process NetworksCode0
Object proposal generation applying the distance dependent Chinese restaurant processCode0
USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MRCode0
Tractable Function-Space Variational Inference in Bayesian Neural NetworksCode0
TRADE: Transfer of Distributions between External Conditions with Normalizing FlowsCode0
Trading Information between Latents in Hierarchical Variational AutoencodersCode0
On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization GuaranteesCode0
On Divergence Measures for Bayesian PseudocoresetsCode0
On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental DesignCode0
A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net ModelsCode0
On Kalman-Bucy filters, linear quadratic control and active inferenceCode0
Bayesian inference of mixed Gaussian phylogenetic modelsCode0
Gradient-Free Adversarial Attacks for Bayesian Neural NetworksCode0
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential FamiliesCode0
Grammar Induction for Minimalist Grammars using Variational Bayesian Inference : A Technical ReportCode0
Graph-based sequential beamformingCode0
Variational Inference for Bayesian Neural Networks under Model and Parameter UncertaintyCode0
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Benchmark Results

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