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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 351–400 of 2226 papers

TitleStatusHype
Learning to infer in recurrent biological networksCode0
Bayesian posterior repartitioning for nested samplingCode0
Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-DivergencesCode0
Bayesian Conditional Density FilteringCode0
Amortized variational transdimensional inferenceCode0
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamicsCode0
Bayesian Convolutional Neural Networks for Compressed Sensing RestorationCode0
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational InferenceCode0
On the representation and learning of monotone triangular transport mapsCode0
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank DeterminationCode0
Accelerated Bayesian imaging by relaxed proximal-point Langevin samplingCode0
Dropout as a Structured Shrinkage PriorCode0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
PDE-constrained Gaussian process surrogate modeling with uncertain data locationsCode0
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic netCode0
Efficient and Transferable Adversarial Examples from Bayesian Neural NetworksCode0
Bayesian Approaches to Shrinkage and Sparse EstimationCode0
Differentially Private Federated Variational InferenceCode0
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable CouplingsCode0
Bayesian adaptive and interpretable functional regression for exposure profilesCode0
Differentially Private Bayesian Learning on Distributed DataCode0
Electricity Spot Prices Forecasting Using Stochastic Volatility ModelsCode0
Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference NetworkCode0
Development of Use-specific High Performance Cyber-Nanomaterial Optical Detectors by Effective Choice of Machine Learning AlgorithmsCode0
Differentially Private Bayesian Inference for Exponential FamiliesCode0
An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimationCode0
Projective Integral Updates for High-Dimensional Variational InferenceCode0
Ensemble transport smoothing. Part I: Unified frameworkCode0
An embedded segmental K-means model for unsupervised segmentation and clustering of speechCode0
Differentially Private Distributed Bayesian Linear Regression with MCMCCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian InferenceCode0
Do Bayesian Variational Autoencoders Know What They Don't Know?Code0
Empirical analysis of representation learning and exploration in neural kernel banditsCode0
Detecting Model Misspecification in Amortized Bayesian Inference with Neural NetworksCode0
Are Normalizing Flows the Key to Unlocking the Exponential Mechanism?Code0
Amortized Variational Inference: When and Why?Code0
Baxter Permutation ProcessCode0
Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference of spatio-temporal heat fluxes in rotating disc systemsCode0
Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQLCode0
Batch Bayesian Optimization via Particle Gradient FlowsCode0
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family ApproximationsCode0
Bayesian imaging inverse problem with SA-Roundtrip prior via HMC-pCN samplerCode0
Faster MCMC for Gaussian Latent Position Network ModelsCode0
Deep Neural Networks as Gaussian ProcessesCode0
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex ModelsCode0
Demonstrating the Continual Learning Capabilities and Practical Application of Discrete-Time Active InferenceCode0
Fidelity of Hyperbolic Space for Bayesian Phylogenetic InferenceCode0
Dependent Multinomial Models Made Easy: Stick Breaking with the Pólya-Gamma AugmentationCode0
Batch Bayesian optimisation via density-ratio estimation with guaranteesCode0
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Benchmark Results

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