https://github.com/baptistar/atm
공식 코드On the representation and learning of monotone triangular transport maps
Papers with Code 아카이브
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/barisalparslan1/statistic_selection_and_mcmc
공식 코드Statistic Selection and MCMC for Differentially Private Bayesian Estimation
https://github.com/barwein/BayesProxyNets
공식 코드Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network
https://github.com/bayesflow-org/SA-ABI
공식 코드https://github.com/bayesflow-org/neural-superstatistics
공식 코드Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models
https://github.com/bayubeta/bgphy
공식 코드https://github.com/benjione/sequentialmeasuretransport.jl
공식 코드Sequential transport maps using SoS density estimation and $α$-divergences
https://github.com/berchuck/vaeST
공식 코드https://github.com/berenslab/fslm_repo
공식 코드Efficient identification of informative features in simulation-based inference
https://github.com/bghojogh/Fitting-Mixture-Distribution
공식 코드https://github.com/bharti-ayush/optimally-weighted_MMD
공식 코드Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
https://github.com/biaslab/ForneyLab.jl
공식 코드A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
https://github.com/biaslab/acc2022-vmpnarmax
공식 코드Variational message passing for online polynomial NARMAX identification
https://github.com/biaslab/automatingmodelcomparison
공식 코드https://github.com/biaslab/reactivemp.jl
공식 코드https://github.com/bigai-ai/evaluate-n-model-social-intelligence
공식 코드Evaluating and Modeling Social Intelligence: A Comparative Study of Human and AI Capabilities
https://github.com/bio-phys/BioEM
공식 코드https://github.com/blackjax-devs/blackjax
공식 코드https://github.com/blayes/adda
공식 코드Asynchronous and Distributed Data Augmentation for Massive Data Settings
https://github.com/blei-lab/ars-reparameterization
공식 코드Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
https://github.com/blei-lab/variational-smc
공식 코드https://github.com/bodin-e/defer
공식 코드Black-box density function estimation using recursive partitioning
https://github.com/bohanwu424/npp
공식 코드Adaptive Nonparametric Perturbations of Parametric Bayesian Models
https://github.com/bonstats/bayesscorecal.jl
공식 코드https://github.com/bookdiver/scoreoperator
공식 코드Infinite-dimensional Diffusion Bridge Simulation via Operator Learning
https://github.com/bradleygramhansen/PyLFPPL
공식 코드LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
https://github.com/brain-research/nngp
공식 코드https://github.com/bridgesra/expm_nf_mimic3_results_code
공식 코드Are Normalizing Flows the Key to Unlocking the Exponential Mechanism?
https://github.com/brightmanu-lang/spn-param-recovery
공식 코드A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net Models
https://github.com/butspeechfit/dvbx
공식 코드https://github.com/cair/tmu
공식 코드Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning
https://github.com/caiziruo/nf-ula
공식 코드NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
https://github.com/cambridge-mlg/expressiveness-approx-bnns
공식 코드On the Expressiveness of Approximate Inference in Bayesian Neural Networks
https://github.com/cambridge-mlg/sampled-laplace
공식 코드Sampling-based inference for large linear models, with application to linearised Laplace
https://github.com/carlesfalco/BInference-TissueCollisions
공식 코드Quantifying tissue growth, shape and collision via continuum models and Bayesian inference
https://github.com/carlesfalco/inferencecellcyclepde
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.