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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 301–350 of 2226 papers

TitleStatusHype
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep NetworksCode0
Greedy inference with structure-exploiting lazy mapsCode0
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian InferenceCode0
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical SystemsCode0
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential FamiliesCode0
An embedded segmental K-means model for unsupervised segmentation and clustering of speechCode0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
Projective Integral Updates for High-Dimensional Variational InferenceCode0
An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimationCode0
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Distilling Model KnowledgeCode0
Adaptive Nonparametric Perturbations of Parametric Bayesian ModelsCode0
Distilling Importance Sampling for Likelihood Free InferenceCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic netCode0
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian InferenceCode0
Discrepancies in Epidemiological Modeling of Aggregated Heterogeneous DataCode0
Accelerated Bayesian imaging by relaxed proximal-point Langevin samplingCode0
Analytical Approximation of the ELBO Gradient in the Context of the Clutter ProblemCode0
Analysis of Markov Jump Processes under Terminal ConstraintsCode0
Amortized variational transdimensional inferenceCode0
Differentially Private Distributed Bayesian Linear Regression with MCMCCode0
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functionsCode0
Differentially Private Federated Variational InferenceCode0
Data-driven Modeling and Inference for Bayesian Gaussian Process ODEs via Double Normalizing FlowsCode0
Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma AugmentationCode0
Demonstrating the Continual Learning Capabilities and Practical Application of Discrete-Time Active InferenceCode0
Detecting structural perturbations from time series with deep learningCode0
Deep Learning and genetic algorithms for cosmological Bayesian inference speed-upCode0
On the representation and learning of monotone triangular transport mapsCode0
Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in KenyaCode0
Development of Use-specific High Performance Cyber-Nanomaterial Optical Detectors by Effective Choice of Machine Learning AlgorithmsCode0
Deep Bayesian inference for seismic imaging with tasksCode0
deBInfer: Bayesian inference for dynamical models of biological systems in RCode0
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable CouplingsCode0
Deep Active Inference as Variational Policy GradientsCode0
Deep Bayesian Structure NetworksCode0
Bayesian adaptive and interpretable functional regression for exposure profilesCode0
Measuring Uncertainty through Bayesian Learning of Deep Neural Network StructureCode0
Bayesian Approaches to Shrinkage and Sparse EstimationCode0
PDE-constrained Gaussian process surrogate modeling with uncertain data locationsCode0
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamicsCode0
Bayesian posterior repartitioning for nested samplingCode0
Learning to infer in recurrent biological networksCode0
Data-driven Approach for Interpolation of Sparse DataCode0
Deep Neural Networks as Gaussian ProcessesCode0
Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference of spatio-temporal heat fluxes in rotating disc systemsCode0
Data Subsampling for Bayesian Neural NetworksCode0
Debiased Bayesian inference for average treatment effectsCode0
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Benchmark Results

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