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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 1251–1300 of 2226 papers

TitleStatusHype
Softplus Regressions and Convex Polytopes—0
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors—0
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning—0
Solving Differential Equations Using Neural Network Solution Bundles—0
Solving #SAT and Bayesian Inference with Backtracking Search—0
Some Extensions of Probabilistic Logic—0
Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model—0
Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation—0
Spatial Uncertainty Sampling for End-to-End Control—0
Spatial-variant causal Bayesian inference for rapid seismic ground failures and impacts estimation—0
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology—0
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors—0
Sparse species interactions reproduce abundance correlation patterns in microbial communities—0
Spectral Reconstruction with Deep Neural Networks—0
SpectraNet: Learned Recognition of Artificial Satellites From High Contrast Spectroscopic Imagery—0
Spike and slab variational Bayes for high dimensional logistic regression—0
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning—0
Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM—0
Spinal Muscle Atrophy Disease Modelling as Bayesian Network—0
Spintronics based Stochastic Computing for Efficient Bayesian Inference System—0
Sprucing up the trees -- Error detection in treebanks—0
Stability and Convergence of Stochastic Particle Flow Filters—0
Stability of Mean-Field Variational Inference—0
Stable generative modeling using Schrödinger bridges—0
Stacking Variational Bayesian Monte Carlo—0
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent—0
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection—0
Statistical applications of contrastive learning—0
Statistical Mechanics of Dynamical System Identification—0
Statistical Mechanics of High-Dimensional Inference—0
Statistical physics, Bayesian inference and neural information processing—0
Stein transport for Bayesian inference—0
Stein Variational Gradient Descent for Approximate Bayesian Computation—0
Stein Variational Gradient Descent: many-particle and long-time asymptotics—0
Stein Variational Model Predictive Control—0
Stein Variational Policy Gradient—0
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation—0
Stochastic Control Barrier Functions with Bayesian Inference for Unknown Stochastic Differential Equations—0
Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging—0
Stochastic full waveform inversion with deep generative prior for uncertainty quantification—0
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent—0
Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference—0
Stochastic Gradient MCMC Methods for Hidden Markov Models—0
Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC—0
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo—0
Stochastic inference with deterministic spiking neurons—0
Stochastic inference with spiking neurons in the high-conductance state—0
Stochasticity from function -- why the Bayesian brain may need no noise—0
A Two-stage Multiband WiFi Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference—0
Stochastic Relational Models for Large-scale Dyadic Data using MCMC—0
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Benchmark Results

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