Care sunt principalele funcționalități ale aloctavodia/statistical-rethinking-with-python-and-pymc3?

Principalele funcționalități ale aloctavodia/statistical-rethinking-with-python-and-pymc3 sunt: Bayesian Probabilistic Programming Frameworks, Markov Chain Monte Carlo Sampling, Bayesian Statistical Modeling, Hierarchical Models, Automatic Differentiation Engines, Computational Notebooks, Statistical Learning Guides, Statistical Simulations.

Care sunt câteva alternative open-source pentru aloctavodia/statistical-rethinking-with-python-and-pymc3?

Alternativele open-source pentru aloctavodia/statistical-rethinking-with-python-and-pymc3 includ: pymc-devs/pymc — PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian… tensorflow/probability — TensorFlow Probability is a library for probabilistic reasoning and statistical analysis integrated with the… blei-lab/edward — Edward is a probabilistic programming language and inference engine designed for building deep generative models and… uber/pyro — Pyro is a probabilistic programming language and library built for PyTorch. It serves as a Bayesian inference engine… camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers — This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for… krasserm/bayesian-machine-learning — This project is an educational collection of computational notebooks and tutorials focused on Bayesian machine…

Statistical Rethinking With Python And PyMC3

Acest repository servește ca resursă educațională pentru modelarea statistică Bayesiană, oferind o colecție de exemple instrucționale care traduc conceptele teoretice în cod Python executabil. Funcționează ca un framework computațional pentru efectuarea inferenței statistice și estimarea parametrilor, conceput pentru a ajuta utilizatorii să învețe și să aplice tehnici de programare probabilistică prin documentație interactivă.

Proiectul utilizează un framework de programare probabilistică pentru a defini modelele statistice ca grafuri aciclice direcționate, permițând inferența automată prin algoritmi avansați de eșantionare. Prin valorificarea eșantionării Hamiltonian Monte Carlo și a diferențierii automate, modelele explorează distribuții de probabilitate multidimensionale pentru a genera eșantioane posterioare. Implementarea se bazează pe calculul vectorial al array-urilor pentru a gestiona operațiuni matematice complexe pe seturi de date simultan.

Colecția acoperă o gamă largă de sarcini de analiză a datelor științifice, inclusiv construcția de modele ierarhice Bayesiene care permit partajarea informațiilor între grupuri. Aceste exemple sunt organizate într-un mediu de notebook computațional, care intercalează text narativ cu cod pentru a documenta procesul iterativ de construire, testare și validare a ipotezelor statistice.

Features

Bayesian Probabilistic Programming Frameworks - Provides an integrated software environment for building probabilistic models and performing Bayesian inference.
Markov Chain Monte Carlo Sampling - Implements algorithms for sampling from complex probability distributions using Markov chains.
Bayesian Statistical Modeling - Applies probabilistic programming to perform complex data analysis and parameter estimation.
Hierarchical Models - Structures statistical parameters in nested layers to allow information sharing across groups and improve overall estimation accuracy.
Automatic Differentiation Engines - Computes gradients of mathematical functions by traversing computational graphs to enable automated inference.
Computational Notebooks - Provides web-based environments for interactive, reproducible data analysis and code execution.
Statistical Learning Guides - Offers educational resources focused on implementing exercises from statistical learning textbooks.
Statistical Simulations - Provides interactive tools for sampling and data fitting to observe probability distributions and Bayesian updates.
Probabilistic Programming - Provides educational resources for building mathematical models to estimate unknown parameters using observed data and priors.
Probabilistic Programming Workflows - Supports the end-to-end process of designing generative models and integrating dynamic systems for inference.

Istoric stele

aloctavodiaStatistical-Rethinking-with-Python-and-PyMC3

Name: aloctavodia/statistical-rethinking-with-python-and-pymc3
Author: aloctavodia

View on GitHub

876 stele255 fork-uriJupyter Notebook9 vizualizări

Statistical Rethinking With Python And PyMC3

Features

Bayesian Probabilistic Programming Frameworks - Provides an integrated software environment for building probabilistic models and performing Bayesian inference.
Markov Chain Monte Carlo Sampling - Implements algorithms for sampling from complex probability distributions using Markov chains.
Bayesian Statistical Modeling - Applies probabilistic programming to perform complex data analysis and parameter estimation.
Hierarchical Models - Structures statistical parameters in nested layers to allow information sharing across groups and improve overall estimation accuracy.
Automatic Differentiation Engines - Computes gradients of mathematical functions by traversing computational graphs to enable automated inference.
Computational Notebooks - Provides web-based environments for interactive, reproducible data analysis and code execution.
Statistical Learning Guides - Offers educational resources focused on implementing exercises from statistical learning textbooks.
Statistical Simulations - Provides interactive tools for sampling and data fitting to observe probability distributions and Bayesian updates.
Probabilistic Programming - Provides educational resources for building mathematical models to estimate unknown parameters using observed data and priors.
Probabilistic Programming Workflows - Supports the end-to-end process of designing generative models and integrating dynamic systems for inference.

Alternative open-source pentru Statistical Rethinking With Python And PyMC3

Proiecte open-source similare, clasificate după numărul de funcționalități comune cu Statistical Rethinking With Python And PyMC3.

pymc-devs/pymc
pymc-devs/pymc
9,650Vezi pe GitHub
PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o
Pythonbayesian-inferencemcmcprobabilistic-programming
Vezi pe GitHub9,650
tensorflow/probability
tensorflow/probability
4,420Vezi pe GitHub
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis integrated with the TensorFlow ecosystem. It serves as a Bayesian deep learning framework, a probabilistic programming interface, and a variational inference engine, providing a toolset for Markov chain Monte Carlo sampling and tensor-based probabilistic modeling. The project enables the construction of neural networks with probabilistic weights and the implementation of Bayesian neural networks to quantify prediction uncertainty. It provides specialized capabilities for hierarchical probabilistic modelin
Jupyter Notebook
Vezi pe GitHub4,420
blei-lab/edward
blei-lab/edward
4,841Vezi pe GitHub
Edward is a probabilistic programming language and inference engine designed for building deep generative models and Bayesian neural networks. It utilizes the TensorFlow framework to represent probabilistic models as differentiable computational graphs. The library enables the construction of complex data distributions through Bayesian neural networks, mixture models, and Gaussian processes. It differentiates itself by providing an integrated toolkit for both supervised and unsupervised probabilistic modeling, including the implementation of generative adversarial networks and mixture density
Jupyter Notebookbayesian-methodsdata-sciencedeep-learning
Vezi pe GitHub4,841
uber/pyro
uber/pyro
9,009Vezi pe GitHub
Pyro is a probabilistic programming language and library built for PyTorch. It serves as a Bayesian inference engine and a tool for probabilistic graphical modeling, allowing users to define generative models that combine neural networks with probabilistic logic. The framework enables deep probabilistic programming by integrating probability distributions into computational graphs. This allows for the quantification of uncertainty in deep learning models and the execution of scalable posterior distribution calculations for complex data dependencies. The system provides a suite of inference c
Python
Vezi pe GitHub9,009

Vezi toate cele 30 alternative pentru Statistical Rethinking With Python And PyMC3

Colecții curatoriate care includ Statistical Rethinking With Python And PyMC3

Colecții selectate manual în care apare Statistical Rethinking With Python And PyMC3.

Statistică și probabilități pentru Data Science

aloctavodiaStatistical-Rethinking-with-Python-and-PyMC3

Statistical Rethinking With Python And PyMC3

Features

Istoric stele

aloctavodiaStatistical-Rethinking-with-Python-and-PyMC3

Statistical Rethinking With Python And PyMC3

Features

Alternative open-source pentru Statistical Rethinking With Python And PyMC3

pymc-devs/pymc

tensorflow/probability

blei-lab/edward

uber/pyro

Colecții curatoriate care includ Statistical Rethinking With Python And PyMC3

Întrebări frecvente

Istoric stele

Colecții curatoriate care includ Statistical Rethinking With Python And PyMC3

Alternative open-source pentru Statistical Rethinking With Python And PyMC3

pymc-devs/pymc

tensorflow/probability

blei-lab/edward

uber/pyro

Întrebări frecvente