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Bayesian Inference

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/daviesl/avti

공식 코드

Amortized variational transdimensional inference

https://github.com/dawenl/vae_cf

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Variational Autoencoders for Collaborative Filtering

https://github.com/ddfan/balsa

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Bayesian Learning-Based Adaptive Control for Safety Critical Systems

https://github.com/deceptrax123/jpeb-gssl

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Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised Learning

https://github.com/deeplearning-wisc/picle

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PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning

https://github.com/deepmind/nonstationary_mbml

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Memory-Based Meta-Learning on Non-Stationary Distributions

https://github.com/deepppl/deepppl

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Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming

https://github.com/dennisprangle/distillingimportancesampling

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Distilling Importance Sampling for Likelihood Free Inference

https://github.com/desmond-ong/pplAffComp

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Applying Probabilistic Programming to Affective Computing

https://github.com/desreslab/invaert4cardio

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InVAErt networks for amortized inference and identifiability analysis of lumped parameter hemodynamic models

https://github.com/digitaldigger/bayesianvaesood

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Do Bayesian Variational Autoencoders Know What They Don't Know?

https://github.com/dilinwang820/matrix_svgd

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Stein Variational Gradient Descent With Matrix-Valued Kernels

https://github.com/dionman/beta-cores

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$β$-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers

https://github.com/dirmeier/dmvi

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Diffusion models for probabilistic programming

https://github.com/dirmeier/ssnl

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Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation

https://github.com/djo1996/Bayesian_Continual_Learning_And_Forgetting

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Bayesian continual learning and forgetting in neural networks

https://github.com/dmml-geneva/calibrated-posterior

공식 코드

Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability

https://github.com/dolgov/TT-IRT

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Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reduction

https://github.com/dongweiye/gaussian-process-learning

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Gaussian process learning of nonlinear dynamics

https://github.com/dongweiye/gp-linearpde-uncertainty

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PDE-constrained Gaussian process surrogate modeling with uncertain data locations

https://github.com/donotdespair/SVAR-MSH-ID

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Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity

https://github.com/dpiras/cosmopower-jax

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CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulators

https://github.com/dpkingma/nips14-ssl

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Semi-Supervised Learning with Deep Generative Models

https://github.com/dsbrown1331/bayesianrex

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Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences

https://github.com/dvav/eQTLBookChapter

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Exploring Bayesian approaches to eQTL mapping through probabilistic programming

https://github.com/ebilionis/variational-reformulation-of-inverse-problems

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Variational Reformulation of Bayesian Inverse Problems

https://github.com/ebonilla/VGCN

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Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

https://github.com/edfong/npl

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Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap

https://github.com/ege-erdil/estimating-returns-to-rnd

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Estimating Idea Production: A Methodological Survey

https://github.com/egetaga/timepfn

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TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data

https://github.com/eilion/SA-GPR

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Bayesian Inference Gaussian Process Multiproxy Alignment of Continuous Signals (BIGMACS): Applications for Paleoceanography

https://github.com/elizavetasemenova/priorCVAE_jax

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PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling

https://github.com/emer/axon

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Locally Learned Synaptic Dropout for Complete Bayesian Inference

https://github.com/emorymlip/cot-flow

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Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

https://github.com/emorymlip/pcp-map

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Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference

https://github.com/enalisnick/dropout_icml2019

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Dropout as a Structured Shrinkage Prior

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