SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1511–1520 of 2226 papers

TitleStatusHype
A Bayesian take on option pricing with Gaussian processes—0
A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction—0
A Latent Variational Framework for Stochastic Optimization—0
ABC random forests for Bayesian parameter inference—0
A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors—0
Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference—0
Absolute Ranking: An Essential Normalization for Benchmarking Optimization Algorithms—0
A category theory framework for Bayesian learning—0
Accelerated Parallel Non-conjugate Sampling for Bayesian Non-parametric Models—0
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning—0
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Benchmark Results

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