Alternatives and similar repositories for PyBRML

Users that are interested in PyBRML are comparing it to the libraries listed below

• CyberPoint / libpgm
  A library for creating and using probabilistic graphical models
  ☆76Updated 7 years ago
• inf16nyu / home
  ☆77Updated 8 years ago
• ronojoy / pylearn
  Bayesian machine learning in Python
  ☆76Updated 9 years ago
• junpenglao / Bayesian-Cognitive-Modeling-in-Pymc3
  PyMC3 codes of Lee and Wagenmakers' Bayesian Cognitive Modeling - A Pratical Course
  ☆96Updated 7 years ago
• xinlin192 / GraphicalModel
  Courera Version of Graphical Model.. Cooperate with Jian Guo.
  ☆122Updated 8 years ago
• cliburn / sta-663-2016
  STA663 Statistical Computing and Computation, Spring 2016
  ☆87Updated 9 years ago
• tdhopper / notes-on-dirichlet-processes
  Notes explaining Dirichlet Processes, HDPs, and Latent Dirichlet Allocation
  ☆414Updated 6 years ago
• edwardlib / papers
  Edward content including papers, posters, and talks
  ☆91Updated 4 years ago
• ReactiveCJ / BayesianLearning
  Bayesian Machine Learning
  ☆207Updated 2 years ago
• powerscorinne / GLRM
  ☆98Updated 7 years ago
• napsternxg / ipython-notebooks
  Some iPython Notebooks I have created for personal learning
  ☆65Updated last year
• BRML / climin
  Optimizers for machine learning
  ☆183Updated last year
• ajtulloch / Elements-of-Statistical-Learning
  Contains LaTeX, SciPy and R code providing solutions to exercises in Elements of Statistical Learning (Hastie, Tibshirani & Friedman)
  ☆290Updated 11 years ago
• jamt9000 / prml
  Some Jupyter notebooks based on Bishop's "Pattern Recognition and Machine Learning" book
  ☆76Updated 5 years ago
• stats385 / stats385.github.io
  STATS385 course website
  ☆89Updated 2 years ago
• liang456 / Stanford-Probabilistic-Graphical-Models-Coursera
  ☆82Updated 8 years ago
• fonnesbeck / PyMC3_DataScienceLA
  PyMC3 tutorial for DataScience LA (January 2017)
  ☆67Updated 7 years ago
• kuleshov / cs228-notes
  Lecture notes on probabilistic graphical modeling, based on Stanford CS228 (work in progress!)
  ☆30Updated 8 years ago
• twiecki / WhileMyMCMCGentlySamples
  my blog
  ☆268Updated 3 years ago
• SheffieldML / notebook
  Collection of jupyter notebooks for demonstrating software.
  ☆167Updated 2 years ago
• tbroderick / bnp_tutorial
  ☆30Updated 7 years ago
• astahlman / MLaPP
  Solutions to exercises from Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
  ☆47Updated 8 years ago
• blei-lab / deep-exponential-families
  Deep exponential families (DEFs)
  ☆55Updated 7 years ago
• craffel / theano-tutorial
  A collection of tutorials on neural networks, using Theano
  ☆222Updated 2 years ago
• anhncs / Probabilistic-Graphical-Models
  Code for Implementation, Inference, and Learning of Bayesian and Markov Networks along with some practical examples.
  ☆104Updated 12 years ago
• amueller / advanced_training
  Advanced Scikit-learn training session
  ☆118Updated 8 years ago
• fabianp / minirank
  Ranking and ordinal regression algorithms in Python
  ☆135Updated 4 years ago
• dillonalaird / pysvihmm
  Implementation of stochastic variational inference for Bayesian hidden Markov models.
  ☆66Updated 7 years ago
• HIPS / Probabilistic-Backpropagation
  Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.
  ☆192Updated 6 years ago
• lamblin / bayareadlschool
  Slides and exercises for the Theano tutorial at the Deep Learning School in Stanford, September 24-25, 2016
  ☆114Updated 8 years ago