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Bayesian Inference

229 papers with code · Methodology

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

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Greatest papers with code

Weight Uncertainty in Neural Networks

20 May 2015tensorflow/models

We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop.

BAYESIAN INFERENCE

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

NeurIPS 2016 pyro-ppl/pyro

We propose a general purpose variational inference algorithm that forms a natural counterpart of gradient descent for optimization.

BAYESIAN INFERENCE VARIATIONAL INFERENCE

ZhuSuan: A Library for Bayesian Deep Learning

18 Sep 2017thu-ml/zhusuan

In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.

PROBABILISTIC PROGRAMMING

Neural Tangents: Fast and Easy Infinite Neural Networks in Python

ICLR 2020 google/neural-tangents

Neural Tangents is a library designed to enable research into infinite-width neural networks.

BAYESIAN INFERENCE

Simulation-Based Inference for Global Health Decisions

14 May 2020mrc-ide/covid-sim

The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.

BAYESIAN INFERENCE EPIDEMIOLOGY

SAME but Different: Fast and High-Quality Gibbs Parameter Estimation

18 Sep 2014BIDData/BIDMach

SAME (State Augmentation for Marginal Estimation) \cite{Doucet99, Doucet02} is an approach to MAP parameter estimation which gives improved parameter estimates over direct Gibbs sampling.

BAYESIAN INFERENCE

A Scalable Laplace Approximation for Neural Networks

ICLR 2018 JavierAntoran/Bayesian-Neural-Networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more

BAYESIAN INFERENCE

Semi-Supervised Learning with Deep Generative Models

NeurIPS 2014 probtorch/probtorch

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis.

BAYESIAN INFERENCE

A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

8 Jan 2019kumar-shridhar/PyTorch-BayesianCNN

In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights.

BAYESIAN INFERENCE IMAGE CLASSIFICATION IMAGE SUPER-RESOLUTION SUPER RESOLUTION VARIATIONAL INFERENCE

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

15 Jun 2018kumar-shridhar/PyTorch-BayesianCNN

On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100), this variational inference method achieves performances equivalent to frequentist inference in identical architectures, while the two desiderata, a measure for uncertainty and regularization are incorporated naturally.

BAYESIAN INFERENCE VARIATIONAL INFERENCE