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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 1291–1300 of 2226 papers

TitleStatusHype
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