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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 1026–1050 of 2226 papers

TitleStatusHype
Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation—0
Asynchronous and Distributed Data Augmentation for Massive Data SettingsCode0
Automatic maneuver detection and tracking of space objects in optical survey scenarios based on stochastic hybrid systems formulation—0
Microbiome subcommunity learning with logistic-tree normal latent Dirichlet allocationCode0
Modeling Massive Spatial Datasets Using a Conjugate Bayesian Linear Regression Framework—0
A theory of representation learning gives a deep generalisation of kernel methods—0
A Mathematical Walkthrough and Discussion of the Free Energy Principle—0
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive ModelsCode0
Modeling Item Response Theory with Stochastic Variational Inference—0
Uncertainty Quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ballCode0
Modeling time evolving COVID-19 uncertainties with density dependent asymptomatic infections and social reinforcement—0
A survey on Bayesian inference for Gaussian mixture model—0
Approximate Bayesian Neural Doppler ImagingCode0
A fast asynchronous MCMC sampler for sparse Bayesian inferenceCode0
Stability and Convergence of Stochastic Particle Flow Filters—0
Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs—0
Dynamic Semantic Occupancy Mapping using 3D Scene Flow and Closed-Form Bayesian InferenceCode1
Deep Stable neural networks: large-width asymptotics and convergence rates—0
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling—0
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning—0
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference—0
Are Bayesian neural networks intrinsically good at out-of-distribution detection?Code0
A Multiple-Instance Learning Approach for the Assessment of Gallbladder Vascularity from Laparoscopic Images—0
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference—0
Neural Variational Gradient DescentCode1
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Benchmark Results

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