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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 1226–1250 of 2226 papers

TitleStatusHype
Semi-parametric financial risk forecasting incorporating multiple realized measures—0
Semiring Programming: A Declarative Framework for Generalized Sum Product Problems—0
Separated-Spectral-Distribution Estimation Based on Bayesian Inference with Single RGB Camera—0
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference—0
Sequential Bayesian Detection of Spike Activities from Fluorescence Observations—0
Sequential Bayesian experimental designs via reinforcement learning—0
Sequential Cooperative Bayesian Inference—0
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Sequential Gibbs Posteriors with Applications to Principal Component Analysis—0
Sequential Monte Carlo Inference of Mixed Membership Stochastic Blockmodels for Dynamic Social Networks—0
Optimality of short-term synaptic plasticity in modelling certain dynamic environments—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Sigma Point Belief Propagation—0
Signal-based Bayesian Seismic Monitoring—0
Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks—0
Simplifying deflation for non-convex optimization with applications in Bayesian inference and topology optimization—0
Simulating how animals learn: a new modelling framework applied to the process of optimal foraging—0
Simulation-based Bayesian inference for robotic grasping—0
Simulation-based Bayesian inference for multi-fingered robotic grasping—0
Simulation-based Bayesian inference under model misspecification—0
Simulation-based inference with the Python Package sbijax—0
Simultaneous Estimation of Noise Variance and Number of Peaks in Bayesian Spectral Deconvolution—0
Sketching for Latent Dirichlet-Categorical Models—0
SMC Is All You Need: Parallel Strong Scaling—0
SoftBart: Soft Bayesian Additive Regression Trees—0
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Benchmark Results

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