Alternatives and similar repositories for pgm

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

• wiseodd / probabilistic-models
  Collection of probabilistic models and inference algorithms
  ☆241Updated 5 years ago
• vsmolyakov / DP_means
  Dirichlet Process K-means
  ☆48Updated last year
• zalandoresearch / probrnn
  State space modeling with recurrent neural networks
  ☆45Updated 7 years ago
• cambridge-mlg / sghmc_dgp
  ☆29Updated 6 years ago
• bertini36 / GMM
  Variational Inference in Gaussian Mixture Model
  ☆59Updated 4 years ago
• suriyadeepan / BayesianML
  Experiments in Bayesian Machine Learning
  ☆69Updated 6 years ago
• jluttine / variational-bayes-book
  Open access book on variational Bayesian methods written collaboratively
  ☆28Updated 10 years ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆37Updated 8 years ago
• dillonalaird / pysvihmm
  Implementation of stochastic variational inference for Bayesian hidden Markov models.
  ☆69Updated 7 years ago
• clinicalml / dmm
  Deep Markov Models
  ☆133Updated 6 years ago
• kasparmartens / NeuralProcesses
  Neural Processes implementation for 1D regression
  ☆65Updated 6 years ago
• edwardlib / papers
  Edward content including papers, posters, and talks
  ☆92Updated 4 years ago
• kekeblom / DeepCGP
  Deep convolutional gaussian processes.
  ☆80Updated 5 years ago
• ncullen93 / pyBN
  Bayesian Networks in Python
  ☆149Updated last year
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆150Updated 6 years ago
• TENGBINN / Bayes-by-Backprop
  TensorFlow implementation of Bayes-by-Backprop algorithm from "Weight Uncertainty in Neural Networks" paper
  ☆51Updated 6 years ago
• joanbruna / ir18
  Inference and Representation (DS-GA-1005, CSCI-GA.2569), fall 18
  ☆67Updated 6 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
• ivannz / mlss2019-bayesian-deep-learning
  MLSS2019 Tutorial on Bayesian Deep Learning
  ☆93Updated 5 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆140Updated 9 years ago
• chrisdxie / in-depth-VI-tutorial
  ☆11Updated 9 years ago
• clinicalml / structuredinference
  Structured Inference Networks for Nonlinear State Space Models
  ☆273Updated 7 years ago
• bsharchilev / influence_boosting
  Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"
  ☆68Updated last year
• sailinglab / pgm-spring-2019
  Course webpage for PGM, Spring 2019.
  ☆76Updated 4 years ago
• tqchen / ML-SGHMC
  Experiment code for Stochastic Gradient Hamiltonian Monte Carlo
  ☆106Updated 7 years ago
• abietti / dpmm
  Variational inference for Dirichlet process mixture models with multinomial mixture components.
  ☆35Updated 11 years ago
• SheffieldML / deepGP
  Deep Gaussian Processes in matlab
  ☆93Updated 3 years ago
• simonkamronn / kvae
  Kalman Variational Auto-Encoder
  ☆136Updated 6 years ago
• akshaykgupta / SG_MCMC
  Implementation of Stochastic Gradient MCMC algorithms
  ☆41Updated 8 years ago
• widedeepnetworks / widedeepnetworks
  Code for the paper Gaussian process behaviour in wide deep networks
  ☆46Updated 6 years ago