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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 1301–1350 of 2226 papers

TitleStatusHype
Stochastic triangular mesh mapping: A terrain mapping technique for autonomous mobile robots—0
Stochastic Variational Bayesian Inference for a Nonlinear Forward Model—0
Scalable Stochastic Parametric Verification with Stochastic Variational Smoothed Model Checking—0
Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment—0
Streaming Bayes GFlowNets—0
Streaming Bayesian Inference for Crowdsourced Classification—0
Streaming Bayesian inference: theoretical limits and mini-batch approximate message-passing—0
String and Membrane Gaussian Processes—0
Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach—0
Subsampling Sequential Monte Carlo for Static Bayesian Models—0
Subspace Constrained Variational Bayesian Inference for Structured Compressive Sensing with a Dynamic Grid—0
Surprise Calibration for Better In-Context Learning—0
Surrogate Likelihoods for Variational Annealed Importance Sampling—0
SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy—0
Switching Linear Dynamics for Variational Bayes Filtering—0
Symbolic Analysis-based Reduced Order Markov Modeling of Time Series Data—0
Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems—0
TapType: Ten-finger text entry on everyday surfaces via Bayesian inference—0
Taxonomy, Structure, and Implementation of Evidential Reasoning—0
Tensor Regression Meets Gaussian Processes—0
SBI -- A toolkit for simulation-based inference—0
Textual Bayes: Quantifying Uncertainty in LLM-Based Systems—0
That's Mine! Learning Ownership Relations and Norms for Robots—0
The Bayesian Approach to Continual Learning: An Overview—0
The "Bayesian" brain, with a bit less Bayes—0
The Bayesian Context Trees State Space Model for time series modelling and forecasting—0
The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation—0
The Case for Bayesian Deep Learning—0
The challenge of uncertainty quantification of large language models in medicine—0
The Compositional Structure of Bayesian Inference—0
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems—0
The equivalence between Stein variational gradient descent and black-box variational inference—0
The FMRIB Variational Bayesian Inference Tutorial II: Stochastic Variational Bayes—0
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity—0
The high-conductance state enables neural sampling in networks of LIF neurons—0
The Inverse of Exact Renormalization Group Flows as Statistical Inference—0
The Kikuchi Hierarchy and Tensor PCA—0
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks—0
The linear conditional expectation in Hilbert space—0
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection—0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning—0
The Population Posterior and Bayesian Modeling on Streams—0
The principles of adaptation in organisms and machines II: Thermodynamics of the Bayesian brain—0
The Recycling Gibbs Sampler for Efficient Learning—0
Thermodynamic Bayesian Inference—0
The Stochastic complexity of spin models: Are pairwise models really simple?—0
The Variational Bayesian Inference for Network Autoregression Models—0
The Variational InfoMax Learning Objective—0
The variational Laplace approach to approximate Bayesian inference—0
Thompson Sampling for Parameterized Markov Decision Processes with Uninformative Actions—0
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Benchmark Results

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