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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://bitbucket.org/dtolpin/stochastic-conditioning
공식 코드https://github.com/AaltoPML/machine-teaching-of-active-sequential-learners
공식 코드https://github.com/BerkeleyLearnVerify/Scenic
공식 코드Scenic: A Language for Scenario Specification and Scene Generation
https://github.com/ExtensityAI/symbolicai
공식 코드SymbolicAI: A framework for logic-based approaches combining generative models and solvers
https://github.com/RSNirwan/HouseholderBPCA
공식 코드Rotation Invariant Householder Parameterization for Bayesian PCA
https://github.com/TuringLang/DynamicPPL.jl
공식 코드DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
https://github.com/adockhorn/markovsenior
공식 코드Markov Senior -- Learning Markov Junior Grammars to Generate User-specified Content
https://github.com/alessiospuriomancini/cosmopower
공식 코드https://github.com/alexluedtke12/dimple
공식 코드Simplifying debiased inference via automatic differentiation and probabilistic programming
https://github.com/alexluedtke12/pydimple
공식 코드Simplifying debiased inference via automatic differentiation and probabilistic programming
https://github.com/amir1m/companies-bankruptcy-forecast
공식 코드Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction
https://github.com/astro-informatics/harmonic
공식 코드https://github.com/biaslab/ForneyLab.jl
공식 코드A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
https://github.com/biaslab/automatingmodelcomparison
공식 코드https://github.com/blackjax-devs/blackjax
공식 코드https://github.com/bradleygramhansen/PyLFPPL
공식 코드LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
https://github.com/christiaanjs/treeflow
공식 코드TreeFlow: probabilistic programming and automatic differentiation for phylogenetics
https://github.com/deepppl/deepppl
공식 코드Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
https://github.com/desmond-ong/pplAffComp
공식 코드https://github.com/dirmeier/dmvi
공식 코드https://github.com/dpiras/cosmopower-jax
공식 코드https://github.com/dvav/eQTLBookChapter
공식 코드Exploring Bayesian approaches to eQTL mapping through probabilistic programming
https://github.com/ethanluoyc/sympais
공식 코드Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
https://github.com/extensityai/benchmark
공식 코드SymbolicAI: A framework for logic-based approaches combining generative models and solvers
https://github.com/facebookresearch/lightweight-inference-compilation
공식 코드Accelerating Metropolis-Hastings with Lightweight Inference Compilation
https://github.com/facebookresearch/pplbench
공식 코드PPL Bench: Evaluation Framework For Probabilistic Programming Languages
https://github.com/fzaiser/genfer
공식 코드https://github.com/fzaiser/nonparametric-hmc
공식 코드https://github.com/gabegrand/world-models
공식 코드https://github.com/gitter-lab/ssps
공식 코드https://github.com/google-research/cascades
공식 코드https://github.com/google-research/google-research/tree/master/automatic_structured_vi
공식 코드https://github.com/google-research/recsim_ng
공식 코드RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
https://github.com/infprobscix/icecores
공식 코드https://github.com/juice-jl/dice.jl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.