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Probabilistic Programming
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Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models. PPLs are closely related to graphical models and Bayesian networks, but are more expressive and flexible.
<span style="color:grey; opacity: 0.6">( Image credit: Michael Betancourt )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lbai-lab/push
공식 코드Push: Concurrent Probabilistic Programming for Bayesian Deep Learning
https://github.com/lll6924/automatically-marginalized-mcmc
공식 코드Automatically Marginalized MCMC in Probabilistic Programming
https://github.com/lll6924/hamiltonian_lme
공식 코드Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
https://github.com/luchinoprince/mixture_is
공식 코드Robust leave-one-out cross-validation for high-dimensional Bayesian models
https://github.com/marius311/muse-implicit-paper
공식 코드Improved Marginal Unbiased Score Expansion (MUSE) via Implicit Differentiation
https://github.com/mgorinova/SlicStan
공식 코드Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
https://github.com/mgorinova/autoreparam
공식 코드https://github.com/ml-research/slash
공식 코드https://github.com/mlglobalhealth/pi-vae
공식 코드$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
https://github.com/mspakkanen/integral-equations
공식 코드Unifying incidence and prevalence under a time-varying general branching process
https://github.com/mullachv/MLExp
공식 코드https://github.com/njuwfang/differentiableqpl
공식 코드https://github.com/oduerr/bayes_cal_paper
공식 코드https://github.com/plai-group/amortized-rejection-sampling
공식 코드Amortized Rejection Sampling in Universal Probabilistic Programming
https://github.com/plai-group/covid
공식 코드https://github.com/probcomp/bayeslite
공식 코드https://github.com/probcomp/hfppl
공식 코드Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs
https://github.com/probcomp/llamppl
공식 코드Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs
https://github.com/probcomp/pclean
공식 코드PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
https://github.com/probcomp/sppl
공식 코드SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
https://github.com/probcomp/threedp3
공식 코드https://github.com/probmods/webppl-daipp
공식 코드https://github.com/pyprob/pyprob
공식 코드Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
https://github.com/pyro-ppl/funsor
공식 코드https://github.com/pyro-ppl/numpyro
공식 코드Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
https://github.com/rgiordan/dadvipaper
공식 코드https://github.com/thu-ml/zhusuan
공식 코드https://github.com/uber/pyro
공식 코드https://github.com/uu-sml/neurips2019-parameter-elimination
공식 코드https://github.com/willieneis/ProBO
공식 코드ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language
https://github.com/wonyeol/static-analysis-for-support-match
공식 코드Towards Verified Stochastic Variational Inference for Probabilistic Programs
https://gitlab.com/desupervised/borch
공식 코드https://github.com/2023-MindSpore-4/Code6/tree/main/RAOD
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
https://github.com/DPBayes/dppp
D3p -- A Python Package for Differentially-Private Probabilistic Programming
https://github.com/GianluigiSilvestri/asvi
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.