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 1–10 of 2226 papers

TitleStatusHype
A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net ModelsCode0
The Bayesian Approach to Continual Learning: An Overview—0
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning—0
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace InferenceCode0
Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM CommunicationsCode0
Bayesian Evolutionary Swarm Architecture: A Formal Epistemic System Grounded in Truth-Based Competition—0
Coherent Track-Before-Detect—0
Bayesian Inference for Left-Truncated Log-Logistic Distributions for Time-to-event Data Analysis—0
Bayesian Epistemology with Weighted Authority: A Formal Architecture for Truth-Promoting Autonomous Scientific Reasoning—0
Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI—0
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Benchmark Results

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