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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 1601–1650 of 2226 papers

TitleStatusHype
Amortized Bayesian Multilevel Models—0
Amortized Bayesian Workflow (Extended Abstract)—0
Amortized Conditional Normalized Maximum Likelihood—0
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation—0
A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posterior—0
A Multiple-Instance Learning Approach for the Assessment of Gallbladder Vascularity from Laparoscopic Images—0
An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System—0
Analog Seizure Detection for Implanted Responsive Neurostimulation—0
Analogy as Nonparametric Bayesian Inference over Relational Systems—0
Analysing Quality of English-Hindi Machine Translation Engine Outputs Using Bayesian Classification—0
Analysis of Bayesian Inference Algorithms by the Dynamical Functional Approach—0
Analysis of Langevin Monte Carlo via convex optimization—0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks—0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Analyzing human feature learning as nonparametric Bayesian inference—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
Learning in Volatile Environments with the Bayes Factor Surprise—0
An easy-to-use empirical likelihood ABC method—0
An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods—0
A Neural Implementation of the Kalman Filter—0
A New Parameterized Family of Stochastic Particle Flow Filters—0
An Interpretable Neural Network for Parameter Inference—0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions—0
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation—0
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics—0
A normative theory of social conflict—0
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation—0
A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach—0
An overview of differentiable particle filters for data-adaptive sequential Bayesian inference—0
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks—0
An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring—0
An Unsupervised Deep Learning Approach for the Wave Equation Inverse Problem—0
A Parameter-Free Learning Automaton Scheme—0
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
A Parzen-based distance between probability measures as an alternative of summary statistics in Approximate Bayesian Computation—0
A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series—0
Applications of the Free Energy Principle to Machine Learning and Neuroscience—0
Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs—0
Approximate Bayesian inference as a gauge theory—0
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC—0
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale—0
Approximate Bayesian inference in spatial environments—0
Approximate Decentralized Bayesian Inference—0
Approximate Gibbs Sampler for Efficient Inference of Hierarchical Bayesian Models for Grouped Count Data—0
Approximate Inference for Spectral Mixture Kernel—0
Approximate Inference with the Variational Holder Bound—0
Approximating Permutations with Neural Network Components for Travelling Photographer Problem—0
Approximation Properties of Variational Bayes for Vector Autoregressions—0
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas—0
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Benchmark Results

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