Bayesian Inference
624 papers with code • 1 benchmarks • 7 datasets
Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).
Libraries
Use these libraries to find Bayesian Inference models and implementationsDatasets
CIFAR-100
WebKB
UCLA Aerial Event Dataset
ExBAN
Most implemented papers
Probabilistic Autoencoder
VMBoehm/PAE
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Under review 2020
The PAE is fast and easy to train and achieves small reconstruction errors, high sample quality, and good performance in downstream tasks.
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$ Model
Many of these methods are based on traditional epidemiological model which rely on simulations or Bayesian inference to simultaneously learn many parameters at a time.
Rule-based Bayesian regression
We introduce a novel rule-based approach for handling regression problems.
Implementing Approximate Bayesian Inference using Adaptive Quadrature: the aghq Package
The aghq package for implementing approximate Bayesian inference using adaptive quadrature is introduced.
Variational Autoencoders for Collaborative Filtering
We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.
BRUNO: A Deep Recurrent Model for Exchangeable Data
IraKorshunova/bruno
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NeurIPS 2018
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
pyprob/pyprob
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NeurIPS 2019
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
Undirected Graphical Models as Approximate Posteriors
rickyHong/Quadrant-qupa-repl
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ICML 2020
We extend the class of posterior models that may be learned by using undirected graphical models.
Functional Variational Bayesian Neural Networks
We introduce functional variational Bayesian neural networks (fBNNs), which maximize an Evidence Lower BOund (ELBO) defined directly on stochastic processes, i. e. distributions over functions.
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function.
