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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 1901–1950 of 2226 papers

TitleStatusHype
Calibration and Filtering of Exponential L\'evy Option Pricing Models—0
Forecasting using incomplete models—0
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution—0
Sharing deep generative representation for perceived image reconstruction from human brain activityCode0
Mixture modeling on related samples by ψ-stick breaking and kernel perturbation—0
Metropolis Sampling—0
Stochastic Gradient Descent as Approximate Bayesian InferenceCode0
Object proposal generation applying the distance dependent Chinese restaurant processCode0
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian ProcessesCode0
Stein Variational Policy Gradient—0
Bayesian Inference of Log Determinants—0
Efficient acquisition rules for model-based approximate Bayesian computation—0
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functionsCode0
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models—0
Cohort effects in mortality modelling: a Bayesian state-space approach—0
An embedded segmental K-means model for unsupervised segmentation and clustering of speechCode0
Inference of epidemiological parameters from household stratified data—0
Differentially Private Bayesian Learning on Distributed DataCode0
The variational Laplace approach to approximate Bayesian inference—0
Signal-based Bayesian Seismic Monitoring—0
Bayesian inference on random simple graphs with power law degree distributions—0
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable CouplingsCode0
Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis—0
The Stochastic complexity of spin models: Are pairwise models really simple?—0
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence ClusteringCode0
Bayesian System Identification based on Hierarchical Sparse Bayesian Learning and Gibbs Sampling with Application to Structural Damage Assessment—0
Bone fusion in normal and pathological development is constrained by the network architecture of the human skull—0
Adversarial Message Passing For Graphical Models—0
Expectation Propagation performs a smoothed gradient descent—0
A Probabilistic Model of Social Decision Making based on Reward Maximization—0
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo—0
Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models—0
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models—0
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics—0
Efficient Likelihood Bayesian Constrained Local Model—0
Adams Conditioning and Likelihood Ratio Transfer Mediated Inference—0
The Recycling Gibbs Sampler for Efficient Learning—0
Generalized Dropout—0
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference—0
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures—0
Variational Bayes In Private Settings (VIPS)Code0
Fuzzy Bayesian LearningCode0
Operator Variational Inference—0
Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach—0
Body movement to sound interface with vector autoregressive hierarchical hidden Markov models—0
Bayesian latent structure discovery from multi-neuron recordingsCode0
Recurrent switching linear dynamical systemsCode0
Stochastic inference with spiking neurons in the high-conductance state—0
Reparameterization Gradients through Acceptance-Rejection Sampling AlgorithmsCode0
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology—0
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Benchmark Results

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