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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 1801–1850 of 2226 papers

TitleStatusHype
A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems—0
Solving Bongard Problems with a Visual Language and Pragmatic Reasoning—0
Mean Field Network based Graph Refinement with application to Airway Tree Extraction—0
Model selection and parameter inference in phylogenetics using Nested SamplingCode0
A Compressed Sensing Approach for Distribution Matching—0
Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference—0
Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models—0
Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Locally Private Bayesian Inference for Count ModelsCode0
Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels—0
A Probabilistic Disease Progression Model for Predicting Future Clinical OutcomeCode0
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity—0
Analysis of Langevin Monte Carlo via convex optimization—0
Conditionally Independent Multiresolution Gaussian ProcessesCode0
BRUNO: A Deep Recurrent Model for Exchangeable DataCode0
Variational Autoencoders for Collaborative FilteringCode0
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization—0
Improving Quadrature for Constrained IntegrandsCode0
Bayesian inference for bivariate ranks—0
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration—0
Weighted Community Detection and Data Clustering Using Message Passing—0
A Review of Multiple Try MCMC algorithms for Signal Processing—0
A Distributed Framework for the Construction of Transport Maps—0
Bayesian reconstruction of HIV transmission trees from viral sequences and uncertain infection times—0
Experimentally detecting a quantum change point via Bayesian inference—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections—0
Interpreting Deep Classification Models With Bayesian Inference—0
A Scalable Laplace Approximation for Neural NetworksCode0
Gaussian Process Neurons—0
On Statistical Optimality of Variational Bayes—0
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators—0
Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise—0
Assumed Density Filtering Q-learningCode0
Approximations in the homogeneous Ising model—0
Episodic memory for continual model learning—0
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit—0
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal NeuronsCode0
Molecular Clock Dating using MrBayes—0
Riemannian Stein Variational Gradient Descent for Bayesian InferenceCode0
On the use of bootstrap with variational inference: Theory, interpretation, and a two-sample test example—0
Gaussian Process Neurons Learn Stochastic Activation Functions—0
A Parameter-Free Learning Automaton Scheme—0
Computing the quality of the Laplace approximation—0
Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data—0
A Kolmogorov-Smirnov test for the molecular clock on Bayesian ensembles of phylogenies—0
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models—0
Bootstrapped synthetic likelihood—0
Message Passing Stein Variational Gradient Descent—0
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Benchmark Results

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